Changes of gut microbiota and short chain fatty acids in patients with Peutz-Jeghers syndrome.

Peutz-Jeghers Syndromeis a rare autosomal dominant genetic disease characterized by gastrointestinal hamartomatous polyps and skin and mucous membrane pigmentation. The pathogenesis of PJS remains unclear; however, it may be associated with mutations in the STK11 gene, and there is currently no effective treatment available. The gut microbiota plays an important role in maintaining intestinal homeostasis in the human body, and an increasing number of studies have reported a relationship between gut microbiota and human health and disease. However, relatively few studies have been conducted on the gut microbiota characteristics of patients with PJS. In this study, we analyzed the characteristics of the gut microbiota of 79 patients with PJS using 16 S sequencing and measured the levels of short-chain fatty acids in the intestines. The results showed dysbiosis in the gut microbiota of patients with PJS, and decreased synthesis of short-chain fatty acids. Bacteroides was positively correlated with maximum polyp length, while Agathobacter was negatively correlated with age of onset. In addition, acetic acid, propionic acid, and butyric acid were positively correlated with the age of onset but negatively correlated with the number of polyps. Furthermore, the butyric acid level was negatively correlated with the frequency of endoscopic surgeries. In contrast, we compared the gut microbiota of STK11-positive and STK11-negative patients with PJS for the first time, but 16 S sequencing analysis revealed no significant differences. Finally, we established a random forest prediction model based on the gut microbiota characteristics of patients to provide a basis for the targeted diagnosis and treatment of PJS in the future.

New insights into the occurrence of continuous cropping obstacles in pea (Pisum sativum L.) from soil bacterial communities, root metabolism and gene transcription.

BACKGROUND: Continuous cropping is a significant obstacle to sustainable development in the pea (Pisum sativum L.) industry, but the underlying mechanisms of this remain unclear. In this study, we used 16 S rDNA sequencing, transcriptomics, and metabolomics to analyze the response mechanism of roots and soil bacteria to continuous cropping and the relationship between soil bacteria and root phenotypes of different pea genotypes (Ding wan 10 and Yun wan 8). RESULTS: Continuous cropping inhibited pea growth, with a greater effect on Ding wan 10 than Yun wan 8. Metabolomics showed that the number of differentially accumulated metabolites (DAMs) in pea roots increased with the number of continuous cropping, and more metabolic pathways were involved. Transcriptomics revealed that the number of differentially expressed genes (DEGs) increased with the number of continuous cropping. Continuous cropping altered the expression of genes involved in plant-pathogen interaction, MAPK signal transduction, and lignin synthesis pathways in pea roots, with more DEGs in Ding wan 10 than in Yun wan 8. The up-regulated expression of genes in the ethylene signal transduction pathway was evident in Ding wan 10. Soil bacterial diversity did not change, but the relative abundance of bacteria significantly responded to continuous cropping. Integrative analysis showed that the bacteria with significant relative abundance in the soil were strongly associated with the antioxidant synthesis and linoleic acid metabolism pathway of pea roots under continuous cropping once. Under continuous cropping twice, the bacteria with significant relative abundance changes were strongly associated with cysteine and methionine metabolism, fatty acid metabolism, phenylpropanoid biosynthesis, terpenoid backbone biosynthesis, linoleic acid, and amino sugar and nucleotide sugar metabolism. CONCLUSION: Ding wan 10 was more sensitive to continuous cropping than Yun wan 8. Continuous cropping times and pea genotypes determined the differences in root metabolic pathways. There were common metabolic pathways in the two pea genotypes in response to continuous cropping, and the DEGs and DAMs in these metabolic pathways were strongly associated with the bacteria with significant changes in relative abundance in the soil. This study provides new insights into obstacles to continuous cropping in peas.

Effects of a Macroporous Resin Extract of Dendrobium officinale Leaves in Rats with Hyperuricemia Induced by Anthropomorphic Unhealthy Lifestyle.

Aim: Hyperuricemia (HUA) has received increased attention in the last few decades due to its global prevalence. Our previous study found that administration of a macroporous resin extract of Dendrobium officinale leaves (DoMRE) to rats with HUA that was induced by exposure to potassium oxazine combined with fructose and a high-purine diet led to a significant reduction in serum uric acid (SUA) levels. The aim of this study was to explore the effects of DoMRE on hyperuricemia induced by anthropomorphic unhealthy lifestyle and to elucidate its possible mechanisms of action. Methods: Dosages (5.0 and 10.0 g/kg/day) of DoMRE were administered to rats daily after induction of HUA by anthropomorphic unhealthy lifestyle for 12 weeks. The levels of UA in the serum, urine, and feces; the levels of creatinine (Cr) in the serum and urine; and the levels of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) in serum were all measured using an automatic biochemical analyzer. The activities of xanthine oxidase (XOD) and adenosine deaminase (ADA) in the serum, liver, and intestine tissue supernatant were measured using appropriate kits for each biological target. The expressions levels of UA transporters (ABCG2 and GLUT9), tight junction (TJ) proteins (ZO-1 and occludin), and inflammatory factors (IL-6, IL-8, and TNF-α) in the intestine were assayed by immunohistochemical (IHC) staining. Hematoxylin and eosin (H&E) staining was used to assess histological changes in the renal and intestinal tissues. Results: DoMRE treatment significantly reduced SUA levels and concomitantly increased fecal UA (FUA) levels and the fractional excretion of UA (FEUA) in HUA rats. Furthermore, DoMRE significantly reduced both the XOD activity in the serum, liver, and intestine and the ADA activity in the liver and intestine. DoMRE also effectively regulated the expression of GLUT9 and ABCG2 in the intestine, and it significantly upregulated the expression of the intestinal TJ proteins ZO-1 and occludin. Therefore, DoMRE reduced the damage to the intestinal barrier function caused by the increased production of inflammatory factors due to HUA to ensure normal intestinal UA excretion. Conclusion: DoMRE demonstrated anti-HUA effects in the HUA rat model induced by an anthropomorphic unhealthy lifestyle, and the molecular mechanism appeared to involve the regulation of urate transport-related transporters (ABCG2 and GLUT9) in the intestine, protection of the intestinal barrier function to promote UA excretion, and inhibition of XOD and ADA activity in the liver and intestine to inhibit UA production in the HUA-induced rats.

Nomogram for predicting cancer-specific survival in undifferentiated pleomorphic sarcoma: A Surveillance, Epidemiology, and End Results -based study.

INTRODUCTION: The purpose of this study was to construct and validate a nomogram for predicting cancer-specific survival (CSS) in undifferentiated pleomorphic sarcoma (UPS) patients at 3, 5, and 8 years after the diagnosis. METHODS: Data for UPS patients were extracted from the SEER (Surveillance, Epidemiology, and End Results) database. The patients were randomly divided into a training cohort (70%) and a validation cohort (30%). The backward stepwise Cox regression model was used to select independent prognostic factors. All of the factors were integrated into the nomogram to predict the CSS rates in UPS patients at 3, 5, and 8 years after the diagnosis. The nomogram' s performance was then validated using multiple indicators, including the area under the time-dependent receiver operating characteristic curve (AUC), consistency index (C-index), calibration curve, decision-curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification improvement (NRI). RESULTS: This study included 2,009 UPS patients. Ten prognostic factors were identified after analysis of the Cox regression model in the training cohort, which were year of diagnosis, age, race, primary site, histological grade, T, N, M stage, surgery status, and insurance status. The nomogram was then constructed and validated internally and externally. The relatively high C-indexes and AUC values indicated that the nomogram has good discrimination ability. The calibration curves revealed that the nomogram was well calibrated. NRI and IDI values were both improved, indicating that our nomogram was superior to the AJCC (American Joint Committee on Cancer) system. DCA curves demonstrated that the nomogram was clinically useful. CONCLUSIONS: The first nomogram for predicting the prognosis of UPS patients has been constructed and validated. Its usability and performance showed that the nomogram can be applied to clinical practice. However, further external validation is still needed.

Competing-risks nomogram for predicting cancer-specific death in upper tract urothelial carcinoma: a population-based analysis.

OBJECTIVE: This study aimed to use a competing-risks model to establish a nomogram to accurately analyse the prognostic factors for upper tract urothelial carcinoma (UTUC) cancer-specific death (CSD). DESIGN: Retrospective observational cohort study. SETTING: The programme has yielded a database of all patients with cancer in 18 defined geographical regions of the USA. PARTICIPANTS: We selected patients with UTUC from the latest edition of the Surveillance, Epidemiology, and End Results database from 1975 to 2016. After excluding patients with unknown histological grade, tumour size and lymph node status, 2576 patients were finally selected. PRIMARY AND SECONDARY OUTCOME MEASURES: We used the Fine-Gray proportional subdistribution hazards model for multivariate analysis and compared the results with cause-specific hazards model. We finally constructed a nomogram for 3-year, 5-year and 8-year CSD rates and tested these rates in a validation cohort. RESULTS: The proportional subdistribution hazards model showed that sex, tumour size, distant metastasis, surgery status, number of lymph nodes positive (LNP) and lymph nodes ratio (LNR) were independent prognostic factors for CSD. All significant factors associated with CSD were included in the nomogram. The 3-year, 5-year and 8-year concordance indexes were 0.719, 0.702 and 0.692 in the training cohort and 0.701, 0.675 and 0.668 in the validation cohort, respectively. CONCLUSIONS: The competing-risks model showed that sex, tumour size, distant metastasis, surgery status, LNP and LNR were associated with CSD. The nomogram predicts the probability of CSD in patients with UTUC at 3, 5 and 8 years, which may help clinicians in predicting survival probabilities in individual patients.

Nomograms for Differentiated Thyroid Carcinoma Patients Based on the Eighth AJCC Staging and Competing Risks Model.

Background: Differentiated thyroid carcinoma (DTC) patients have a long survival period and good prognosis, so they are easily affected by competing risk events. The purpose of this study was to use the competing risks model to identify prognostic factors for cause-specific death (CSD) and death due to other causes (DOC) in patients with DTC. Methods: Our screening process identified 34 585 DTC patients in the Surveillance, Epidemiology, and End Results database and randomly divided them into a training cohort and a validation cohort. We used the Fine and Gray subdistribution hazards model to establish the CSD and DOC nomograms. The distinguishing ability and consistency of the nomograms were evaluated using the consistency indexes and calibration plots. Results: Our analysis of a competing risks model revealed that pathological grade, tumor size, histological type, American Joint Committee on Cancer (AJCC)-8 stage, surgery status, adjuvant radiotherapy status, adjuvant chemotherapy status, and log odds of positive lymph nodes are prognostic factors for CSD, and age at diagnosis, year of diagnosis, sex, pathological grade, tumor size, AJCC-8 stage, surgery status, adjuvant radiotherapy status, and lymph node ratio are prognostic factors for DOC. The 1-year, 3-year, and 5-year concordance indexes in the validation cohorts were 0.942, 0.931, and 0.913 for the CSD nomogram and 0.813, 0.746, and 0.776 for the DOC nomogram. The calibration plots showed good consistency in both nomograms. Conclusions: Our nomograms can be used as a tool to help clinicians individually predict the probability of CSD and DOC in DTC patients at 1 year, 3 years, and 5 years, which has certain guiding value in clinical applications.

Nonlinear Relationship Between Age and Likelihood of Undergoing Prostate-Specific Antigen Testing, and the Predictive Factors of Testing at Different Ages.

OBJECTIVE: To investigate the nonlinear relationship between age and the likelihood of undergoing prostate-specific antigen (PSA) testing, and the difference of factors influencing the test likelihood among subjects aged 40-54, 55-69, and ≥70 years. METHODS: Data were extracted from the 2018 Behavioral Risk Factor Surveillance System, with the primary outcome defined as receipt of a PSA test within the previous 12 months. Restricted cubic splines were used to assess the relationship between age and the likelihood of undergoing PSA testing. Backward conditional logistic regression analyses were used to identify the predictors of undergoing PSA testing among subjects aged 40-54, 55-69, and ≥70 years. RESULTS: Finally, 92,177 people were identified. The likelihood of PSA testing increased up to around 71 years old and then decreased rapidly for higher ages, showing a clear nonlinear inverted U-shaped relationship with age (p < .001). Insurance status, shared decision-making, whether a recommendation for PSA testing had been accepted, income level, smoking status, and age were the common predictors of testing in the three age groups. However, the predictors differed somewhat among the three groups: being overweight or obese was only positively associated with increased testing among people aged 40-54 and ≥70 years, being retired only greatly impacted the test likelihood among those aged 40-54 years, and the general health status, marital status, and race affected people aged ≥55 years. CONCLUSION: The factors influencing PSA screening differ with age, which should be fully considered when screening different target age groups.

Competing-risks nomograms for predicting cause-specific mortality in parotid-gland carcinoma: A population-based analysis.

INTRODUCTION: Parotid-gland carcinoma (PGC) is a relatively rare tumor that comprises a group of heterogeneous histologic subtypes. We used the Surveillance, Epidemiology, and End Results (SEER) program database to apply a competing-risks analysis to PGC patients, and then established and validated predictive nomograms for PGC. METHODS: Specific screening criteria were applied to identify PGC patients and extract their clinical and other characteristics from the SEER database. We used the cumulative incidence function to estimate the cumulative incidence rates of PGC-specific death (GCD) and other cause-specific death (OCD), and tested for differences between groups using Gray's test. We then identified independent prognostic factors by applying the Fine-Gray proportional subdistribution hazard approach, and constructed predictive nomograms based on the results. Calibration curves and the concordance index (C-index) were employed to validate the nomograms. RESULTS: We finally identified 4,075 eligible PGC patients who had been added to the SEER database from 2004 to 2015. Their 1-, 3-, and 5-year cumulative incidence rates of GCD were 10.1%, 21.6%, and 25.7%, respectively, while those of OCD were 2.9%, 6.6%, and 9.0%. Age, race, World Health Organization histologic risk classification, differentiation grade, American Joint Committee on Cancer (AJCC) T stage, AJCC N stage, AJCC M stage, and RS (radiotherapy and surgery status) were independent predictors of GCD, while those of OCD were age, sex, marital status, AJCC T stage, AJCC M stage, and RS. These factors were integrated for constructing predictive nomograms. The results for calibration curves and the C-index suggested that the nomograms were well calibrated and had good discrimination ability. CONCLUSION: We have used the SEER database to establish-to the best of our knowledge-the first competing-risks nomograms for predicting the 1-, 3-, and 5-year cause-specific mortality in PGC. The nomograms showed relatively good performance and can be used in clinical practice to assist clinicians in individualized treatment decision-making.

Developing and validating a novel nomogram used a competing-risks model for predicting the prognosis of primary fallopian tube carcinoma: a retrospective study based on the SEER database.

BACKGROUND: The current prognostic methods for primary fallopian tube carcinoma (PFTC) are inadequate. This study is the first to use a competing-risks model to perform an accurate analysis of the prognostic factors for PFTC cause-specific death (CSD). We used the model to established a nomogram for the 3-, 5-, and 8-year CSD rates based on the identified prognostic factors. METHODS: This study selected 1,924 patients from the SEER (Surveillance, Epidemiology, and End Results) database. The cumulative incidence function (CIF) was used in univariate analyses, and Gray's test was used to determine the intergroup difference in the CIF. We then used the subdistribution proportional hazards model in a multivariate analysis. We finally used the prognostic factors identified in the analysis of the competing-risks model to construct a 3-, 5-, and 8-year CSD nomogram for PFTC patients. The concordance index (C-index) and calibration plots were used to evaluate the discrimination ability and consistency of the model. RESULTS: The subdistribution proportional hazards model showed that age, histological type, FIGO stage, and the log of the ratio between the numbers of positive and negative lymph nodes (LODDS) were independent prognostic factors for CSD. The 3-, 5-, and 8-year C-indexes were 0.744, 0.744, and 0.733 in the training cohort, and 0.737, 0.748, and 0.721 in the validation cohort. In the calibration plots, the forecast lines were very close to the reference lines. CONCLUSIONS: This study is the first to analyze the prognostic factors for PFTC based on a competing-risks model. This model indicates that age, histological type, FIGO stage, and LODDS are significant prognostic factors affecting CSD in PFTC patients. We have also constructed the first 3-, 5-, and 8-year CSD nomogram for PFTC patients. This nomogram exhibits good discrimination ability and accuracy and can help clinicians to provide individualized prognostic analysis for PFTC patients.

Nomogram based on immune scores for predicting the survival of patients with esophageal squamous cell carcinoma.

OBJECTIVE: To explore the relationship between immune scores and prognosis of patients with esophageal squamous cell carcinoma (ESCC) and construct a corresponding clinical prediction model. METHODS: The present research was a retrospective cohort study. We obtained the clinical information and immune scores of 137 patients with ESCC from The Cancer Genome Atlas database, and a Cox proportional risk model was used to construct the clinical prediction model. The concordance index, receiver operating characteristic curve, calibration curve, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate model performance and prediction accuracy. RESULTS: Patients with a high immune score (> -121.4) showed a worse prognosis than those with a low immune score (< -645.8; hazard ratio=3.743, 95% confidence interval [CI]=1.385-10.115, P=0.009). The concordance index of the predictive model was 0.733 (95% CI=0.655-0.812). The calibration curve showed that the 3- and 5-year overall survival rates predicted by the model were highly consistent with the observed values. The NRI and IDI for the 3-year overall survival indicated that the model with the immune scores was superior for classifying the risk probability and distinguishing cases. CONCLUSION: Immune scores may be an independent predictor of prognosis in patients with ESCC.

A pan-cancer study of selenoprotein genes as promising targets for cancer therapy.

BACKGROUND: The most important health benefit of selenium (Se) is in the prevention and control of cancer. Glutathione peroxidases (GPXs) and thioredoxin reductases (TXNRDs) are selenoenzymes that are thought to play a role in oxidative stress. The differential expression of genes of the TXNRD and GPX families is closely related to carcinogenesis and the occurrence of cancer. This study comprehensively analyzed the expression profiles of seven genes in the TXNRD and GPX families, in terms of their correlations with patient survival and immune-cell subtypes, tumor microenvironment, and drug sensitivity. RESULTS: The expression profiles of genes in the TXNRD and GPX families differ between different types of cancer, and also between and within individual cancer cases. The expression levels of the seven analyzed genes are related to the overall survival of patients. The TXNRD1 and TXNRD3 genes are mainly related to poor prognoses, while other genes are related to good or poor prognoses depending on the type of cancer. All of the genes were found to be correlated to varying degrees with immune-cell subtypes, level of mechanistic cell infiltration, and tumor cell stemness. The TXNRD1, GPX1, and GPX2 genes may exert dual effects in tumor mutagenesis and development, while the TXNRD1, GPX1, GPX2, and GPX3 genes were found to be related to drug sensitivity or the formation of drug resistance. CONCLUSIONS: The results will greatly help in identifying the association between genes and tumorigenesis, especially in the immune response, tumor microenvironment, and drug resistance, and very important when attempting to identify new therapeutic targets.

Inhibiting the PI3K/AKT/mTOR signalling pathway with copper oxide nanoparticles from Houttuynia cordata plant: attenuating the proliferation of cervical cancer cells.

Cervical cancer is the most important female genital cancer that develops from the cervix, a lower part of uterus. Houttuynia cordata is ubiquitously present in Asian countries, and traditionally prescribed to treat infections and oedema. Our study emphasizes on biological synthesis route for developing copper nanocomplex using Houttuynia cordata (Hc-CuONPs) plant extract. The UV-visible spectroscopy study of Hc-CuONPs revealed the maximum peak at 350 nm, which proved the formation of Hc-CuONPs and FT-IR absorption peaks revealed the existence of different functional groups. The results of high-resolution TEM and X-ray diffraction studies revealed that the Hc-CuONPs have face centred cubic structure along with 40-45 nm in size. The temperature conditions of the synthesized Hc-CuONPs were spherical and circular morphologies. Furthermore, the Hc-CuONPs (IC50=5 µg/ml) exhibited toxicity on cervical cancer cells (HeLa). The intracellular reactive oxygen species (ROS) level in the control and Hc-CuONPs-treated HeLa cells was monitored by DCFH-DA staining and the apoptotic cell death was detected by using the dual (AO/EtBr) staining, propidium iodide and DAPI staining assays. Our results from the fluorescent staining analysis evidenced that the Hc-CuONPs have inhibited the cell proliferation and promoted the apoptotic cell death in HeLa cells. The Hc-CuONPs promoted the apoptosis by targeting the PI3K/Akt signalling pathways in HeLa cells. Our results explored that the Hc-CuONPs are effective against in vitro HeLa cancer cells.

Competitive Risk Analysis of Prognosis in Patients With Cecum Cancer: A Population-Based Study.

BACKGROUND: The presence of competing risks means that the results obtained using the classic Cox proportional-hazards model for the factors affecting the prognosis of patients diagnosed with cecum cancer (CC) may be biased. OBJECTIVE: The purpose of this study was to establish a competitive risk model for patients diagnosed with CC to evaluate the relevant factors affecting the prognosis of patients, and to compare the results with the classical COX proportional risk model. METHODS: We extracted data on patients diagnosed with CC registered between 2004 and 2016 in the Surveillance, Epidemiology, and End Results (SEER) database. The univariate analysis utilized the cumulative incidence function and Gray's test, while a multivariate analysis was performed using the Fine-Gray, cause-specific (CS), and Cox proportional-hazards models. RESULTS: The 54463 eligible patients diagnosed with CC included 24387 who died: 12087 from CC and 12300 from other causes. The multivariate Fine-Gray analysis indicated that significant factors affecting the prognosis of patients diagnosed with CC include: age, race, AJCC stage, differentiation grade, tumor size, surgery, radiotherapy, chemotherapy and regional lymph nodes metastasis. Due to the presence of competitive risk events, COX model results could not provide accurate estimates of effects and false-negative results occurred. In addition, COX model misestimated the direction of association between regional lymph node metastasis and cumulative risk of death in patients diagnosed with CC. Competitive risk models tend to be more advantageous when analyzing clinical survival data with multiple endpoints. CONCLUSIONS: The present study can help clinicians to make better clinical decisions and provide patients diagnosed with CC with better support.

Postoperative Adjuvant Chemotherapy Improved the Prognosis in Locally Advanced Cervical Cancer Patients With Optimal Response to Neoadjuvant Chemotherapy.

BACKGROUND: Few studies investigated the effectiveness of adjuvant chemotherapy (ACT) in patients with optimal response to neoadjuvant chemotherapy (NACT), and an optimal number of treatment cycles for these patients remains unknown. METHODS: A total of 261 Chinese patients with FIGO stage IB2-IIB cervical cancer who obtained an optimal response to NACT were included after radical surgery, and the disease-free survival (DFS) and overall survival (OS) of these patients treated with different cycles of postoperative ACT were compared using the Log-rank test and multivariate analysis. RESULTS: We found that the prognosis of optimal responders treated with postoperative ACT was significantly better than those without further adjuvant therapy. The multivariate analysis showed that postoperative ACT was an independent prognostic factor for DFS. However, there was no significant difference in the DFS and OS between patients who had three cycles of ACT and those with six cycles. Further analysis revealed a significant association of six cycles of ACT with the risk of leukopenia, nausea/vomiting, and rash. CONCLUSION: Our data suggest that additional three cycles of ACT after surgery may improve the clinical outcome of optimal responders in terms of DFS, OS, and drug toxicity.

Association between physical activity and kidney stones based on dose-response analyses using restricted cubic splines.

BACKGROUND: This study aimed to determine whether there is a dose-response relationship between physical activity and the self-reported prevalence of kidney stone, based on a restricted cubic splines (RCS) method. METHODS: This study analyzed 8931 adults aged ≥20 years who had participated in the National Health and Nutrition Examination Survey (NHANES) during 2013-16. Kidney stones and physical activity were defined using a standard questionnaire, and metabolic equivalents (MET) were used to quantify the physical activity level. Logistic regression was used to assess the association between physical activity and the risk of kidney stones, and the dose-response relationship was explored using RCS. RESULTS: Kidney stones were present in 10.3% of the analyzed individuals: 11.5% of males and 9.2% of females. After adjusting for potential confounders, compared with the first quartile (Q1) of MET, the odds ratios (ORs) of kidney stones for those with Q2, Q3 and Q4 of MET were 0.72 [95% confidence interval (CI)=0.59-0.87], 0.77 (95% CI = 0.63-0.93) and 0.63 (95% CI = 0.51-0.78), respectively (all P < 0.01). The RCS regression showed that physical activity was related to kidney stones in a non-linear manner (P for non-linearity = 0.0100). The prevalence of kidney stones decreasing as physical activity increased, reaching a plateau for physical activity at approximately 2480 MET-min week-1 (OR = 0.75, 95% CI = 0.63-0.91). CONCLUSIONS: Physical activity is inversely associated with the prevalence of kidney stones, and the dose-response relationship has a plateau, after which the prevalence of kidney stones does not change with the increase of physical activity.

An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm.

Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In the brain tumor segmentation method based on deep learning, the convolutional network model has a good brain segmentation effect. The deep convolutional network model has the problems of a large number of parameters and large loss of information in the encoding and decoding process. This paper proposes a deep convolutional neural network fusion support vector machine algorithm (DCNN-F-SVM). The proposed brain tumor segmentation model is mainly divided into three stages. In the first stage, a deep convolutional neural network is trained to learn the mapping from image space to tumor marker space. In the second stage, the predicted labels obtained from the deep convolutional neural network training are input into the integrated support vector machine classifier together with the test images. In the third stage, a deep convolutional neural network and an integrated support vector machine are connected in series to train a deep classifier. Run each model on the BraTS dataset and the self-made dataset to segment brain tumors. The segmentation results show that the performance of the proposed model is significantly better than the deep convolutional neural network and the integrated SVM classifier.

Incidence of and sociological risk factors for suicide death in patients with leukemia: A population-based study.

OBJECTIVES: Suicide is closely related to sociological factors, but sociological analyses of suicide risk in leukemia are lacking. This study is the first to use the Surveillance, Epidemiology, and End Results Program (SEER) database to analyze sociological risk factors for suicide death in leukemia patients. METHODS: A retrospective search of the SEER database was conducted. Logistic regression was used to identify independent risk factors for suicide death. Variables significant in the univariate logistic regression models were subsequently analyzed using multivariate regression. RESULTS: The death rate was highest in California (1.73%). Suicide mortality was more common during the 1970s and 1980s, after which it trended downward. Young age at diagnosis (18-34 vs. >64 years: odds ratio [OR] = 1.537, 95% confidence interval [CI] = 1.007-2.347; 35-64 vs. >64 years: OR = 1.610, 95% CI = 1.309-1.979), being male (OR = 1.518, 95% CI = 1.230-1.873), and living where a high proportion of people have at least a bachelor's degree (>50% vs. <20%: OR = 8.115, 95% CI = 5.053-13.034) significantly increased suicide death risk. CONCLUSION: Our findings could increase clinician awareness of and appropriate support for leukemia patients at risk of death by suicide.

CaMKII(δ) regulates osteoclastogenesis through ERK, JNK, and p38 MAPKs and CREB signalling pathway.

Calcium/calmodulin-dependent protein kinases (CaMKs) are a group of important molecules mediating calcium signal transmission and have been proved to participate in osteoclastogenesis regulation. CaMKII, a subtype of CaMKs is expressed during osteoclast differentiation, but its role in osteoclastogenesis regulation remains controversial. In the present study, we identified that both mRNA and protein levels of CaMKII (δ) were upregulated in a time-dependent manner during osteoclast differentiation. CaMKII (δ) gene silencing significantly inhibited osteoclast formation, bone resorption, and expression of osteoclast-related genes, including nuclear factor of activated T cells c1 (NFATc1), tartrate-resistant acid phosphatase (TRAP), and c-Src. Furthermore, CaMKII (δ) gene silencing downregulated phosphorylation of mitogen-activated protein kinases (MAPKs), including JNK, ERK, and p38, which were transiently activated by RANKL. Specific inhibitors of ERK, JNK, and p38 also markedly inhibited expression of osteoclast-related genes, osteoclast formation, and bone resorption like CaMKII (δ) gene silencing. Additionally, CaMKII (δ) gene silencing also suppressed RANKL-triggered CREB phosphorylation. Collectively, these data demonstrate the important role of CaMKII (δ) in osteoclastogenesis regulation through JNK, ERK, and p38 MAPKs and CREB pathway.

Competing-risks model for predicting the prognosis of penile cancer based on the SEER database.

OBJECTIVES: This study performed a competing-risks analysis using data from the SEER database on penile cancer patients with the aim of identifying more accurate prognostic factors. METHODS: Data on patients with penile cancer were extracted from the SEER database. A univariate analysis used the cumulative incidence function and Gray's test, while multivariate analysis was performed using the Fine-Gray model. Cumulative hazards were compared with a competing-risks model constructed using Kaplan-Meier estimation. RESULTS: The multivariate Fine-Gray analysis indicated that being black (HR = 1.51, 95%CI: 1.10-2.07, P = .01), AJCC stage II (HR = 1.94, 95%CI: 1.36-2.77, P < .001), AJCC stage III (HR = 1.98, 95%CI: 1.34-2.91, P < .001), tumor size > 5 cm (HR = 2.23, 95%CI: 1.33-3.72, P < .05), and TNM stages N1 (HR = 2.49, 95%CI: 1.71-3.61, P < .001), N2 (HR = 3.25, 95%CI: 2.18-4.84, P < .001), N3 (HR = 5.05, 95%CI: 2.69-9.50, P < .001), and M1 (HR = 2.21, 95%CI: 1.28-3.84, P < .05) were statistically significant. The results obtained using multivariate Cox regression were different, while Kaplan-Meier curve analysis led to an overestimation of the cumulative risk of the patient. CONCLUSIONS: This study established a competing-risks analysis model for the first time based on the SEER database for the risk assessment of penile cancer patients. The results may help clinicians to better understand penile cancer and provide these patients with more appropriate support.