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Purpose: To explore the feasibility of peroral endoscopic myotomy (POEM) in patients with achalasia and hiatal hernia. Materials and methods: We performed a retrospective review of 2136 patients with achalasia between January 2016 and December 2022. Patients with achalasia and hiatal hernia were assigned into study group, and matched patients with achalasia but no hiatal hernia were assigned into control group. The preoperative baseline information, procedure-related adverse events (AEs) and follow-up data were compared between the two groups. Results: Hiatal hernia was identified in 56/1564 (3.6%) patients with achalasia. All of these patients underwent POEM with success. The preoperative baseline characteristics were similar between the study and control group. The study group presented with a similar rate of mucosal injury (12.5% vs 16.1, P = 0.589), pneumothorax (3.6% vs 1.8%, P = 1.000), pleural effusion (8.9% vs 12.5%, P = 0.541) and major AEs (1.8% vs 1.8%, P = 1.000) compared with the control group. As for the follow-up data, no significant differences were observed in clinical success (96.4% vs 92.9%, P = 0.679; 93.6% vs 94.0%, P = 1.000; 86.5% vs 91.4%, P = 0.711) clinical reflux (25.0% vs 19.6%, P = 0.496; 31.9% vs 26.0%, P = 0.521; 35.1% vs 31.4%, P = 0.739) and proton pump inhibitor usage (17.9% vs 16.1%, P = 0.801; 29.8% vs 24.0%, P = 0.520; 32.4% vs 25.7%, P = 0.531) between the study group and control group at 1-year, 2-year and 3-year follow-ups. Conclusions: POEM is a safe and effective treatment for achalasia combined with hiatal hernia. [ABSTRACT FROM AUTHOR]

Hippocampal neurons maintain the ability of proliferation throughout life to support neurogenesis. Deoxynivalenol (DON) is a mycotoxin that exhibits brain toxicity, yet whether and how DON affects hippocampal neurogenesis remains unknown. Here, we use mouse hippocampal neuron cells (HT-22) as a model to illustrate the effects of DON on neuron proliferation and to explore underlying mechanisms. DON exposure significantly inhibits the proliferation of HT-22 cells, which is associated with an up-regulation of cell cycle inhibitor p21 at both mRNA and protein levels. Global and site-specific m6A methylation levels on the 3'UTR of p21 mRNA are significantly increased in response to DON treatment, whereas inhibition of m6A hypermethylation significantly alleviates DON-induced cell cycle arrest. Further mechanistic studies indicate that the m6A readers YTHDF1 and IGF2BP1 are responsible for m6A-mediated increase in p21 mRNA stability. Meanwhile, 3'UTR of E3 ubiquitin ligase TRIM21 mRNA is also m6A hypermethylated, and another m6A reader YTHDF2 binds to the m6A sites, leading to decreased TRIM21 mRNA stability. Consequently, TRIM21 suppression impairs ubiquitin-mediated p21 protein degradation. Taken together, m6A-mediated upregulation of p21, at both post-transcriptional and post-translational levels, contributes to DON-induced inhibition of hippocampal neuron proliferation. These results may provide new insights for epigenetic therapy of neurodegenerative diseases. DON inhibits the proliferation of HT-22 cells. RNA m6A hypermethylation on the transcript of p21 enhances the mRNA stability in a YTHDF1- and IGF2BP1-dependent manner, which leads to the upregulation of p21. RNA m6A hypermethylation on the transcript of TRIM21 decreases the mRNA stability in a YTHDF2-dependent manner, which contributes to prevent p21 ubiquitin-mediated degradation. High expression of p21 contributes to inhibit cell proliferation. [ABSTRACT FROM AUTHOR]

To efficiently estimate the central subspace in sufficient dimension reduction, response discretization via slicing its range is one of the most used methodologies when inverse regression-based methods are applied. However, existing slicing schemes are almost all ad hoc and not widely accepted. Thus, how to define data-driven schemes with certain optimal properties is a longstanding problem in this field. The research described herewith is then two-fold. First, we introduce a likelihood-ratio-based framework for dimension reduction, subsuming the popularly used methods including the sliced inverse regression, the sliced average variance estimation and the likelihood acquired direction. Second, we propose a regularized log likelihood-ratio criterion to obtain a data-driven slicing scheme and derive the asymptotic properties of the estimators. A simulation study is carried out to examine the performance of the proposed method and that of existing methods. A data set concerning concrete compressive strength is also analyzed for illustration and comparison. [ABSTRACT FROM AUTHOR]

Objectives: To determine the relationship between renal tumor complexity and vascular complications after partial nephrectomy using PADUA, RENAL, and ZS scores. Methods: Between January 2007 and December 2018, a total of 1917 patients with available cross-sectional imaging were enrolled in the study. Logistic regressions were used to identify independent predictors of vascular complications. Results: Of 1917 patients, 31 (1.6%) developed vascular complications, including 10 females and 21 males. The high-complexity category was significantly associated with a decreased risk of vascular complication in PADUA (OR = 0.256; 95%CI = 0.086–0.762; P = 0.014) and ZS score (OR = 0.279; 95%CI = 0.083–0.946; P = 0.040). Laparoscopic partial nephrectomy and robot-assisted laparoscopic partial nephrectomy were independent risk factors for vascular complications. Meanwhile, the incidence was significantly reduced in the recent 4 years in the high score tumor group alone in PADUA (0.2% [1/474] vs. 2.2% [3/139], P = 0.038) and ZS score (0.2% [1/469] vs. 2.7% [3/112], P = 0.024). In the first 8 years, laparoscopic partial nephrectomy and robot-assisted laparoscopic partial nephrectomy were the only two independent risk factors for vascular complications. In the recent 4 years, only the high-complexity category was significantly associated with a decreased risk of vascular complication in the PADUA score (OR = 0.110; 95%CI = 0.013–0.938; P = 0.044). Conclusion: The renal anatomic classification system cannot predict the occurrence of vascular complications after partial nephrectomy. [ABSTRACT FROM AUTHOR]

Purpose: To develop an early diagnosis model of prostate cancer based on clinical-radiomics to improve the accuracy of imaging diagnosis of prostate cancer. Methods: The multicenter study enrolled a total of 449 patients with prostate cancer from December 2017 to January 2022. We retrospectively collected information from 342 patients who underwent prostate biopsy at Minhang Hospital. We extracted T2WI images through 3D-Slice, and used mask tools to mark the prostate area manually. The radiomics features were extracted by Python using the “Pyradiomics” module. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for data dimensionality reduction and feature selection, and the radiomics score was calculated according to the correlation coefficients. Multivariate logistic regression analysis was used to develop predictive models. We incorporated the radiomics score, PI-RADS, and clinical features, and this was presented as a nomogram. The model was validated using a cohort of 107 patients from the Xuhui Hospital. Results: In total, 110 effective radiomics features were extracted. Finally, 9 features were significantly associated with the diagnosis of prostate cancer, from which we calculated the radiomics score. The predictors contained in the individualized prediction nomogram included age, fPSA/tPSA, PI-RADS, and radiomics score. The clinical-radiomics model showed good discrimination in the validation cohort (C-index = 0.88). Conclusion: This study presents a clinical-radiomics model that incorporates age, fPSA/PSA, PI-RADS, and radiomics score, which can be conveniently used to facilitate individualized prediction of prostate cancer before prostate biopsy. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) is one of the four most common cancers in the world. At present, human beings have stepped into an aging society, and the number of over eighties colorectal cancer patients has increased year by year. However, few high-quality studies focused on the post-operation complications and long-term outcomes of octogenarian patients with colorectal cancer. This meta-analysis, based on published studies, aims to assess the safety of treating octogenarian CRC patients with surgery. Methods: Databases, including PubMed, Embase, and Cochrane Library were searched until July 2022. The incidence of preoperative comorbidities, postoperative complications, and mortality was assessed using odds ratios (ORs) with corresponding 95% confidence intervals (CIs). Furthermore, the hazard ratios (HRs) with 95% CIs were applied for survival outcomes. Results: A total of 13,790 patients with CRC in 21 studies were included. Our results demonstrated that octogenarian patients were associated with a higher burden of comorbidities (OR = 3.03; 95% CI: 2.03, 4.53; P =.000), high incidences of overall postoperative complications (OR = 1.63; 95% CI: 1.29, 2.06; P =.000), high internal medicine postoperative complications (OR = 2.38; 95% CI: 1.76, 3.21; P =.000), high in-hospital mortality (OR = 4.01; 95% CI: 3.06, 5.27; P =.000) and poor overall survival (OR = 2.13; 95% CI: 1.78, 2.55; P =.000). But there is no statistical difference in surgery-related postoperative complications(OR = 1.16; 95% CI: 0.94, 1.43; P =.16) and DFS (OR = 1.03; 95% CI: 0.83, 1.29; P =.775). Conclusions: Extremely elderly patients with colorectal cancer have the high burden of comorbidities, high postoperative complications and mortality. However, survival outcomes (DFS) in patients 80 years and older are similar to younger patients. Clinicians should administer individualized treatment for such patients. Physiologic age rather than chronological age should determine cancer management for each individual. [ABSTRACT FROM AUTHOR]

Purpose: As Internet technology evolves, electronic health (e-health) literacy gradually becomes a key factor in healthy behaviors and health-related decision-making. However, little is known about the influencing factors of e-health literacy among cancer survivors. Thus, the objective of this study was to systematically review the status quo, assessment tools, and influencing factors of e-health literacy in cancer patients. Methods: We conducted a comprehensive search in several databases, including PubMed, MEDLINE, Embase, CINAHL, PsycInfo, Cochrane Library, China National Knowledge Infrastructure, Wanfang Database, Chinese BioMedical Literature Database, and Chinese Science and Technology Journal Database between January 2000 and December 2021. Results: A total of nine articles were included in this review, all of which were cross-sectional studies. Following the JBI critical appraisal tool, seven of them were rated as high quality. The e-Health Literacy Scale (eHEALS) was the most commonly used measurement for e-health literacy in cancer patients. The level of e-health literacy in cancer survivors was not high, which was associated with a variable of factors. The behavioral model of health services use was adopted to summarize related influencing factors. From an individual's perspective, predisposing characteristics and enabling resources were the most significant factors, without factors related to needs characteristics. Conclusion: The study has identified the influencing factors of e-health literacy among cancer survivors, including age, gender, domicile place, education level, information-seeking behavior, and social support. In the future, e-health literacy lectures need to be carried out for elderly cancer patients, especially those who live in rural areas and have no access to the Internet. Families and friends of cancer survivors should also be encouraged to offer them more support. Implications for Cancer Survivors: These findings of this review provide novel insights for both family members and medical workers to improve e-health literacy in cancer patients. Further research is required to develop easy-to-use electronic health information acquisition devices and establish propagable e-health literacy intervention programs for cancer survivors. [ABSTRACT FROM AUTHOR]

Purpose: To predict survival prognosis of renal cell carcinoma (RCC) patients with tumors larger than 7 cm by preoperative radiological morphological features. Methods: We reviewed the medical records of RCC patients with tumors larger than 7 cm from 2007 to 2017 in Zhongshan Hospital, Fudan University. A total of 251 patients' clinical data were collected. 25 and 9 patients were excluded due to loss of follow-up and lack of imaging data, respectively. PFS and OS from date of surgery were evaluated. We defined the irregularity of the tumor as the morphological feature studied and quantified it according to a theorem of the ellipse: the length from the midpoint of the ellipse to any point on the ellipse is shorter than or equal to 1/2 of the long axis. The cutoff value of irregularity was calculated based on the ROC curve. Cox proportional hazards regression models were used to test associations between features and outcome. Results: Of all the 217 patients included in the study, 67 patients had disease progression and 30 patients died. The cutoff value of the irregularity was selected to be 0.5335. Adrenal invasion, presence of distant metastasis and irregularity of tumors were significantly associated with PFS, and presence of distant metastasis and irregularity of tumors were significantly associated with OS. Conclusions: For patients with tumors larger than 7 cm in RCC, we found a radiological index that is closely related to the prognosis: irregularity. This is an unreported independent prognostic risk factor that can be quantified before surgery. [ABSTRACT FROM AUTHOR]

In this paper, we consider how to select both the fixed effects and the random effects in linear mixed models. To make variable selection more efficient for such models in which there are high correlations between covariates associated with fixed and random effects, a novel approach is proposed, which orthogonalizes fixed and random effects such that the two sets of effects can be separately selected with less influence on one another. Also, unlike most of existing methods with parametric assumptions, the new method only needs fourth order moments of involved random variables. The oracle property is proved. the performance of our method is examined by a simulation study. [ABSTRACT FROM AUTHOR]

We study generalized varying coefficient partially linear models when some linear covariates are error prone, but their ancillary variables are available. We first calibrate the error-prone covariates, then develop a quasi-likelihood-based estimation procedure. To select significant variables in the parametric part, we develop a penalized quasi-likelihood variable selection procedure, and the resulting penalized estimators are shown to be asymptotically normal and have the oracle property. Moreover, to select significant variables in the nonparametric component, we investigate asymptotic behavior of the semiparametric generalized likelihood ratio test. The limiting null distribution is shown to follow a Chi-square distribution, and a new Wilks phenomenon is unveiled in the context of error-prone semiparametric modeling. Simulation studies and a real data analysis are conducted to evaluate the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

We consider marginal generalized partially linear single-index models for longitudinal data. A profile generalized estimating equations (GEE)-based approach is proposed to estimate unknown regression parameters. Within a wide range of bandwidths for estimating the nonparametric function, our profile GEE estimator is consistent and asymptotically normal even if the covariance structure is misspecified. Moreover, if the covariance structure is correctly specified, the semiparametric efficiency can be achieved under heteroscedasticity and without distributional assumptions on the covariates. Simulation studies are conducted to evaluate the finite sample performance of the proposed procedure. The proposed methodology is further illustrated through a data analysis. [ABSTRACT FROM AUTHOR]

The goal of this paper is to develop a double penalized hierarchical likelihood (DPHL) with a modified Cholesky decomposition for simultaneously selecting fixed and random effects in mixed effects models. DPHL avoids the use of data likelihood, which usually involves a high-dimensional integral, to define an objective function for variable selection. The resulting DPHL-based estimator enjoys the oracle properties with no requirement on the convexity of loss function. Moreover, a two-stage algorithm is proposed to effectively implement this approach. An H-likelihood-based Bayesian information criterion (BIC) is developed for tuning parameter selection. Simulated data and a real data set are examined to illustrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we present a method for estimating the conditional distribution function of the model error. Given the covariates, the conditional mean function is modeled as a partial linear model, and the conditional distribution function of model error is modeled as a single-index model. To estimate the single-index parameter, we propose a semi-parametric global weighted least-squares estimator coupled with an indicator function of the residuals. We derive a residual-based kernel estimator to estimate the unknown conditional distribution function. Asymptotic distributions of the proposed estimators are derived, and the residual-based kernel process constructed by the estimator of the conditional distribution function is shown to converge to a Gaussian process. Simulation studies are conducted and a real dataset is analyzed to demonstrate the performance of the proposed estimators. [ABSTRACT FROM AUTHOR]

For analyzing correlated binary data with high-dimensional covariates, we, in this paper, propose a two-stage shrinkage approach. First, we construct a weighted least-squares (WLS) type function using a special weighting scheme on the non-conservative vector field of the generalized estimating equations (GEE) model. Second, we define a penalized WLS in the spirit of the adaptive LASSO for simultaneous variable selection and parameter estimation. The proposed procedure enjoys the oracle properties in high-dimensional framework where the number of parameters grows to infinity with the number of clusters. Moreover, we prove the consistency of the sandwich formula of the covariance matrix even when the working correlation matrix is misspecified. For the selection of tuning parameter, we develop a consistent penalized quadratic form (PQF) function criterion. The performance of the proposed method is assessed through a comparison with the existing methods and through an application to a crossover trial in a pain relief study. [ABSTRACT FROM AUTHOR]