Regulation of spermatogonial stem cell differentiation by Sertoli cells-derived exosomes through paracrine and autocrine signaling.

In the orchestrated environment of the testicular niche, the equilibrium between self-renewal and differentiation of spermatogonial stem cells (SSCs) is meticulously maintained, ensuring a stable stem cell reserve and robust spermatogenesis. Within this milieu, extracellular vesicles, specifically exosomes, have emerged as critical conveyors of intercellular communication. Despite their recognized significance, the implications of testicular exosomes in modulating SSC fate remain incompletely characterized. Given the fundamental support and regulatory influence of Sertoli cells (SCs) on SSCs, we were compelled to explore the role of SC-derived exosomes (SC-EXOs) in the SSC-testicular niche. Our investigation hinged on the hypothesis that SC-EXOs, secreted by SCs from the testes of 5-day-old mice-a developmental juncture marking the onset of SSC differentiation-participate in the regulation of this process. We discovered that exposure to SC-EXOs resulted in an upsurge of PLZF, MVH, and STRA8 expression in SSC cultures, concomitant with a diminution of ID4 and GFRA1 levels. Intriguingly, obstructing exosomal communication in a SC-SSC coculture system with the exosome inhibitor GW4869 attenuated SSC differentiation, suggesting that SC-EXOs may modulate this process via paracrine signaling. Further scrutiny revealed the presence of miR-493-5p within SC-EXOs, which suppresses Gdnf mRNA in SCs to indirectly restrain SSC differentiation through the modulation of GDNF expression-an indication of autocrine regulation. Collectively, our findings illuminate the complex regulatory schema by which SC-EXOs affect SSC differentiation, offering novel perspectives and laying the groundwork for future preclinical and clinical investigations.

Androgen blockage impairs proliferation and function of Sertoli cells via Wee1 and Lfng.

BACKGROUND: Androgens are essential hormones for testicular development and the maintenance of male fertility. Environmental factors, stress, aging, and psychological conditions can disrupt androgen production, impacting the androgen signaling pathway and consequently spermatogenesis. Within the testes, testosterone is produced by Leydig cells and acts on Sertoli cells by activating the androgen receptor (AR), which then translocates to the nucleus to function as a transcription factor. Despite clinical correlations between low testosterone levels and diminished sperm quality, the precise mechanism remains unclear. METHODS: This study explores the hypothesis that reduced androgen levels impair Sertoli cell function by disrupting AR transcriptional regulation. Using an androgen blockade model with enzalutamide, we investigated the impact of low androgen levels on AR target genes in Sertoli cells through ChIP-seq and RNA-seq assays. RESULTS: Our results reveal that androgen blockage increases AR enrichment on the promoter region of Wee1, promoting Wee1 expression, while decreasing binding to the promoter region of Lfng, inhibiting its expression. Increased WEE1 protein inhibits Sertoli cell proliferation, whereas reduced LFNG affects Notch modification, leading to decreased production of glial cell line-derived neurotrophic factor (GDNF), a key growth factor for spermatogonial stem cell self-renewal. CONCLUSIONS: These findings provide new insights into the molecular mechanisms by which low androgen levels interfere with Sertoli cell functions, offering novel perspectives for the clinical treatment of male reproductive disorders.

Integrating randomized controlled trials and non-randomized studies of interventions to assess the effect of rare events: a Bayesian re-analysis of two meta-analyses.

BACKGROUND: There is a growing trend to include non-randomised studies of interventions (NRSIs) in rare events meta-analyses of randomised controlled trials (RCTs) to complement the evidence from the latter. An important consideration when combining RCTs and NRSIs is how to address potential bias and down-weighting of NRSIs in the pooled estimates. The aim of this study is to explore the use of a power prior approach in a Bayesian framework for integrating RCTs and NRSIs to assess the effect of rare events. METHODS: We proposed a method of specifying the down-weighting factor based on judgments of the relative magnitude (no information, and low, moderate, serious and critical risk of bias) of the overall risk of bias for each NRSI using the ROBINS-I tool. The methods were illustrated using two meta-analyses, with particular interest in the risk of diabetic ketoacidosis (DKA) in patients using sodium/glucose cotransporter-2 (SGLT-2) inhibitors compared with active comparators, and the association between low-dose methotrexate exposure and melanoma. RESULTS: No significant results were observed for these two analyses when the data from RCTs only were pooled (risk of DKA: OR = 0.82, 95% confidence interval (CI): 0.25-2.69; risk of melanoma: OR = 1.94, 95%CI: 0.72-5.27). When RCTs and NRSIs were directly combined without distinction in the same meta-analysis, both meta-analyses showed significant results (risk of DKA: OR = 1.50, 95%CI: 1.11-2.03; risk of melanoma: OR = 1.16, 95%CI: 1.08-1.24). Using Bayesian analysis to account for NRSI bias, there was a 90% probability of an increased risk of DKA in users receiving SGLT-2 inhibitors and an 91% probability of an increased risk of melanoma in patients using low-dose methotrexate. CONCLUSIONS: Our study showed that including NRSIs in a meta-analysis of RCTs for rare events could increase the certainty and comprehensiveness of the evidence. The estimates obtained from NRSIs are generally considered to be biased, and the possible influence of NRSIs on the certainty of the combined evidence needs to be carefully investigated.

Adaptive designs were primarily used but inadequately reported in early phase drug trials.

BACKGROUND: Faced with the high cost and limited efficiency of classical randomized controlled trials, researchers are increasingly applying adaptive designs to speed up the development of new drugs. However, the application of adaptive design to drug randomized controlled trials (RCTs) and whether the reporting is adequate are unclear. Thus, this study aimed to summarize the epidemiological characteristics of the relevant trials and assess their reporting quality by the Adaptive designs CONSORT Extension (ACE) checklist. METHODS: We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL) and ClinicalTrials.gov from inception to January 2020. We included drug RCTs that explicitly claimed to be adaptive trials or used any type of adaptative design. We extracted the epidemiological characteristics of included studies to summarize their adaptive design application. We assessed the reporting quality of the trials by Adaptive designs CONSORT Extension (ACE) checklist. Univariable and multivariable linear regression models were used to the association of four prespecified factors with the quality of reporting. RESULTS: Our survey included 108 adaptive trials. We found that adaptive design has been increasingly applied over the years, and was commonly used in phase II trials (n = 45, 41.7%). The primary reasons for using adaptive design were to speed the trial and facilitate decision-making (n = 24, 22.2%), maximize the benefit of participants (n = 21, 19.4%), and reduce the total sample size (n = 15, 13.9%). Group sequential design (n = 63, 58.3%) was the most frequently applied method, followed by adaptive randomization design (n = 26, 24.1%), and adaptive dose-finding design (n = 24, 22.2%). The proportion of adherence to the ACE checklist of 26 topics ranged from 7.4 to 99.1%, with eight topics being adequately reported (i.e., level of adherence ≥ 80%), and eight others being poorly reported (i.e., level of adherence ≤ 30%). In addition, among the seven items specific for adaptive trials, three were poorly reported: accessibility to statistical analysis plan (n = 8, 7.4%), measures for confidentiality (n = 14, 13.0%), and assessments of similarity between interim stages (n = 25, 23.1%). The mean score of the ACE checklist was 13.9 (standard deviation [SD], 3.5) out of 26. According to our multivariable regression analysis, later published trials (estimated ß = 0.14, p < 0.01) and the multicenter trials (estimated ß = 2.22, p < 0.01) were associated with better reporting. CONCLUSION: Adaptive design has shown an increasing use over the years, and was primarily applied to early phase drug trials. However, the reporting quality of adaptive trials is suboptimal, and substantial efforts are needed to improve the reporting.

Design and statistical analysis reporting among interrupted time series studies in drug utilization research: a cross-sectional survey.

INTRODUCTION: Interrupted time series (ITS) design is a commonly used method for evaluating large-scale interventions in clinical practice or public health. However, improperly using this method can lead to biased results. OBJECTIVE: To investigate design and statistical analysis characteristics of drug utilization studies using ITS design, and give recommendations for improvements. METHODS: A literature search was conducted based on PubMed from January 2021 to December 2021. We included original articles that used ITS design to investigate drug utilization without restriction on study population or outcome types. A structured, pilot-tested questionnaire was developed to extract information regarding study characteristics and details about design and statistical analysis. RESULTS: We included 153 eligible studies. Among those, 28.1% (43/153) clearly explained the rationale for using the ITS design and 13.7% (21/153) clarified the rationale of using the specified ITS model structure. One hundred and forty-nine studies used aggregated data to do ITS analysis, and 20.8% (31/149) clarified the rationale for the number of time points. The consideration of autocorrelation, non-stationary and seasonality was often lacking among those studies, and only 14 studies mentioned all of three methodological issues. Missing data was mentioned in 31 studies. Only 39.22% (60/153) reported the regression models, while 15 studies gave the incorrect interpretation of level change due to time parameterization. Time-varying participant characteristics were considered in 24 studies. In 97 studies containing hierarchical data, 23 studies clarified the heterogeneity among clusters and used statistical methods to address this issue. CONCLUSION: The quality of design and statistical analyses in ITS studies for drug utilization remains unsatisfactory. Three emerging methodological issues warranted particular attention, including incorrect interpretation of level change due to time parameterization, time-varying participant characteristics and hierarchical data analysis. We offered specific recommendations about the design, analysis and reporting of the ITS study.

A population-based cohort of drug exposures and adverse pregnancy outcomes in China (DEEP): rationale, design, and baseline characteristics.

The DEEP cohort is the first population-based cohort of pregnant population in China that longitudinally documented drug uses throughout the pregnancy life course and adverse pregnancy outcomes. The main goal of the study aims to monitor and evaluate the safety of drug use through the pregnancy life course in the Chinese setting. The DEEP cohort is developed primarily based on the population-based data platforms in Xiamen, a municipal city of 5 million population in southeast China. Based on these data platforms, we developed a pregnancy database that documented health care services and outcomes in the maternal and other departments. For identifying drug uses, we developed a drug prescription database using electronic healthcare records documented in the platforms across the primary, secondary and tertiary hospitals. By linking these two databases, we developed the DEEP cohort. All the pregnant women and their offspring in Xiamen are provided with health care and followed up according to standard protocols, and the primary adverse outcomes - congenital malformations - are collected using a standardized Case Report Form. From January 2013 to December 2021, the DEEP cohort included 564,740 pregnancies among 470,137 mothers, and documented 526,276 live births, 14,090 miscarriages and 6,058 fetal deaths/stillbirths and 25,723 continuing pregnancies. In total, 13,284,982 prescriptions were documented, in which 2,096 chemicals drugs, 163 biological products, 847 Chinese patent medicines and 655 herbal medicines were prescribed. The overall incidence rate of congenital malformations was 2.0% (10,444/526,276), while there were 25,526 (4.9%) preterm births and 25,605 (4.9%) live births with low birth weight.

Comparing clinical outcomes of peroral endoscopic myotomy for achalasia between Eastern and Western countries: a systematic review and meta-analysis.

Peroral endoscopic myotomy (POEM) has revolutionized the therapeutic strategy for achalasia with promising results. We conducted this meta-analysis to compare clinical outcomes between Eastern and Western countries. A comprehensive literature search was conducted in PubMed, EMBASE, Web of Science and Cochrane Library databases to query for studies that assessed the efficacy of POEM for achalasia. All articles published from inception to December 31, 2021 were included. The primary outcome was the pooled clinical success rate. The secondary outcomes included the pooled technical success rate, incidence of adverse events, procedure time and hospital stay. Eighteen Eastern studies involving 5962 patients and 11 Western studies involving 1651 patients were included. The pooled clinical success rate and technical success rate for POEM was equal in the Eastern studies compared to Western studies. The pooled incidence of procedure adverse events for POEM was a little lower in the Eastern studies compared to Western studies (6.6% vs. 8.7%). Similarly, the incidence of reflux-related adverse events was lower in Eastern studies than that in Western studies. The pooled procedure time of POEM was shorter in Eastern studies compared to Western studies (61 minutes vs. 80 minutes), while the length of hospital stay was longer in Eastern studies compared to Western studies (5.8 days vs. 2.4 days). Overall, Eastern countries have the similar POEM outcomes compared to Western countries. However, Eastern countries still need to do more to reduce the length of hospital stay.

Comparison of Prior Distributions for the Heterogeneity Parameter in a Rare Events Meta-Analysis of a Few Studies.

Bayesian meta-analysis is a promising approach for rare events meta-analysis. However, the inference of the overall effect in rare events meta-analysis is sensitive to the choice of prior distribution for the heterogeneity parameter. Therefore, it is crucial to assign a convincing prior specification and ensure that it is both plausible and transparent. Various priors for the heterogeneity parameter have been proposed; however, the comparative performance of alternative prior specifications in rare events meta-analysis is poorly understood. Based on a binomial-normal hierarchical model, we conducted a comprehensive simulation study to compare seven heterogeneity prior specifications for binary outcomes, using the odds ratio as the metric. We compared their performance in terms of coverage, median percentage bias, width of the 95% credible interval, and root mean square error (RMSE). We illustrate the results with two recently published rare events meta-analyses of a few studies. The results show that the half-normal prior (with a scale of 0.5), the prior proposed by Turner et al. for the general healthcare setting (without restriction to a specific type of outcome) and for the adverse event setting perform well when the degree of heterogeneity is not relatively high, yielding smaller bias and shorter interval widths with similar coverage and RMSE in most cases compared to other prior specifications. None of the priors performed better when the heterogeneity between-studies were significantly extreme.

Comparing various Bayesian random-effects models for pooling randomized controlled trials with rare events.

The meta-analysis of rare events presents unique methodological challenges owing to the small number of events. Bayesian methods are often used to combine rare events data to inform decision-making, as they can incorporate prior information and handle studies with zero events without the need for continuity corrections. However, the comparative performances of different Bayesian models in pooling rare events data are not well understood. We conducted a simulation to compare the statistical properties of four parameterizations based on the binomial-normal hierarchical model, using two different priors for the treatment effect: weakly informative prior (WIP) and non-informative prior (NIP), pooling randomized controlled trials with rare events using the odds ratio metric. We also considered the beta-binomial model proposed by Kuss and the random intercept and slope generalized linear mixed models. The simulation scenarios varied based on the treatment effect, sample size ratio between the treatment and control arms, and level of heterogeneity. Performance was evaluated using median bias, root mean square error, median width of 95% credible or confidence intervals, coverage, Type I error, and empirical power. Two reviews are used to illustrate these methods. The results demonstrate that the WIP outperforms the NIP within the same model structure. Among the compared models, the model that included the treatment effect parameter in the risk model for the control arm did not perform well. Our findings confirm that rare events meta-analysis faces the challenge of being underpowered, highlighting the importance of reporting the power of results in empirical studies.

The Effects of Pore Defects in π-Extended Pentadecabenzo[9]helicene.

The introduction of precise pore defects into nanocarbon structures results in the emergence of distinct physicochemical characteristics. However, there is a lack of research on non-planar chiral nanographene involving precise pore defects. Herein, we have developed two analogues to the π-extended pentadecabenzo[9]helicene (EP9H) containing embedded pore defects. Each molecules, namely extended dodecabenzo[7]helicene (ED7H; 1) or extended nonabenzo[5]helicene (EN5H; 2), exhibits dual-state emission. Significantly, the value of |glum| of 1 is exceptionally high at 1.41×10-2 in solution and BCPL as 254 M-1 cm-1. In PMMA film, |glum| of 1 is 8.56×10-3, and in powder film, it is 5.00×10-3. This study demonstrates that nanocarbon molecules with pore defects exhibit dual-state emission properties while maintaining quite good chiral luminescence properties. It was distinguished from the aggregation-caused quenching (ACQ) effect corresponding to the nanocarbon without embedded defect. Incorporating pore defects into chiral nanocarbon molecules also simplifies the synthesis process and enhances the solubility of the resulting product. These findings suggest that the introduction of pore defects can be a viable approach to improve nanocarbon molecules.

Role of early growth response 1 in inflammation-associated lung diseases.

Early growth response 1 (EGR1), which is involved in cell proliferation, differentiation, apoptosis, adhesion, migration, and immune and inflammatory responses, is a zinc finger transcription factor. EGR1 is a member of the EGR family of early response genes and can be activated by external stimuli such as neurotransmitters, cytokines, hormones, endotoxins, hypoxia, and oxidative stress. EGR1 expression is upregulated during several common respiratory diseases, such as acute lung injury/acute respiratory distress syndrome, chronic obstructive pulmonary disease, asthma, pneumonia, and novel coronavirus disease 2019. Inflammatory response is the common pathophysiological basis of these common respiratory diseases. EGR1 is highly expressed early in the disease, amplifying pathological signals from the extracellular environment and driving disease progression. Thus, EGR1 may be a target for early and effective intervention in these inflammation-associated lung diseases.

Quality and consistency of clinical practice guideline recommendations for PET/CT and PET: a systematic appraisal.

OBJECTIVES: To systematically appraise the methodologies used for guidelines for positron emission tomography (PET) imaging and to compare the consistency of these recommendations. METHODS: We searched PubMed, EMBASE, four guideline databases, and Google Scholar to identify evidence-based clinical practice guidelines pertaining to the use of PET, PET/computed tomography (CT), or PET/magnetic resonance in routine practice. We assessed the quality of each guideline using the Appraisal of Guidelines for Research and Evaluation II instrument and compared recommendations regarding indications for 18F-fluorodeoxyglucose (FDG) PET/CT. RESULTS: Thirty-five guidelines for PET imaging, published between 2008 and 2021, were included. These guidelines performed well in the domains of scope and purpose (median 80.6%, inter-quartile range [IQR] 77.8-83.3%) and clarity of presentation (median 75%, IQR 69.4-83.3%), but poorly in applicability (median 27.1%, IQR 22.9-37.5%). Recommendations for 48 indications in 13 cancers were compared. Considerable inconsistencies in the direction of whether to support the use of FDG PET/CT were observed in 10 (20.1%) indications pertaining to 8 cancer types: head and neck cancer (treatment response assessment), colorectal cancer (staging in patients with stages I-III disease), esophageal cancer (staging), breast cancer (restaging and treatment response assessment), cervical cancer (staging in patients with stage < IB2 disease and treatment response assessment), ovarian cancer (restaging), pancreatic cancer (diagnosis), and sarcoma (treatment response assessment). CONCLUSIONS: Current guidelines for PET imaging vary in methodological quality and provided considerably inconsistent recommendations. Efforts are needed to improve adherence to guideline development methodologies, to synthesis high-quality evidence, and to adopt standard terminologies. PROTOCOL REGISTRATION NUMBER: PROSPERO CRD42020184965. CLINICAL RELEVANCE STATEMENT: Guidelines for PET imaging provide considerably inconsistent recommendations and vary in methodological quality. It is suggested that clinicians be critical of these recommendations when applying them in practice, that guideline developers adopt more rigorous development methodologies, and that researchers prioritize research gaps identified by current guidelines. KEY POINTS: • PET guidelines vary in methodological quality and provided inconsistent recommendations. Efforts are needed to improve methodologies, synthesize high-quality evidence, and standardize terminologies. • Among six domains of methodological quality assessed by the AGREE II tool, guidelines for PET imaging performed well in scope and purpose (median 80.6%, inter-quartile range 77.8-83.3%) and clarity of presentation (75%, 69.4-83.3%), but poorly in applicability (27.1%, 22.9-37.5%). • Among the 48 recommendations (for 13 cancer types) compared, conflicts in the direction of whether to support FDG PET/CT use were observed in 10 (20.1%), for 8 cancer types (i.e., head and neck, colorectal, esophageal, breast, cervical, ovarian, pancreatic, and sarcoma).

Prediction of in-hospital Mortality of Intensive Care Unit Patients with Acute Pancreatitis Based on an Explainable Machine Learning Algorithm.

BACKGROUND AND AIM: Acute pancreatitis (AP) is potentially fatal. Therefore, early identification of patients at a high mortality risk and timely intervention are essential. This study aimed to establish an explainable machine-learning model for predicting in-hospital mortality of intensive care unit (ICU) patients with AP. METHODS: Data on patients with AP, including demographics, vital signs, laboratory tests, comorbidities, treatment, complication, and severity scores, were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and the eICU collaborative research database (eICU-CRD). Based on the data from MIMIC-IV, we used the least absolute shrinkage and selection operator algorithm to select variables and then established 9 machine-learning models and screened the optimal model. Data from the eICU-CRD were used for external validation. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, decision curve, and calibration plots were used to assess the models' efficacy. Shapley's additive explanation values were used to explain the model. RESULTS: Gaussian naive Bayes (GNB) model had the best performance on the data from MIMIC-IV, with an AUC, accuracy, sensitivity, and specificity of 0.840, 0.787, 0.839, and 0.792, respectively. The GNB model also performed well on the data from the eICU-CRD, with an AUC, accuracy, sensitivity, and specificity of 0.862, 0.833, 0.848, and 0.763, respectively. According to Shapley's additive explanation values, the top 4 predictive factors were maximum red cell distribution width, minimum saturation of blood oxygen, maximum blood urea nitrogen, and the Sequential Organ Failure Assessment score. CONCLUSION: The GNB model demonstrated excellent performance and generalizability in predicting mortality in ICU patients with AP. Therefore, it can identify patients at a high mortality risk.

Application of a Machine Learning Predictive Model for Recurrent Acute Pancreatitis.

BACKGROUND AND AIM: Acute pancreatitis is the main cause of hospitalization for pancreatic disease. Some patients tend to have recurrent episodes after experiencing an episode of acute pancreatitis. This study aimed to construct predictive models for recurrent acute pancreatitis (RAP). METHODS: A total of 531 patients who were hospitalized for the first episode of acute pancreatitis at the Affiliated Hospital of Southwest Medical University from January 2018 to December 2019 were enrolled in the study. We confirmed whether the patients had a second episode until December 31, 2021, through an electronic medical record system and telephone or WeChat follow-up. Clinical and follow-up data of patients were collected and randomly allocated to the training and test sets at a ratio of 7:3. The training set was used to select the best model, and the selected model was tested with the test set. The area under the receiver operating characteristic curves, sensitivity, specificity, positive predictive value, negative predictive value, accuracy, decision curve, and calibration plots were used to assess the efficacy of the models. Shapley additive explanation values were used to explain the model. RESULTS: Considering multiple indices, XGBoost was the best model. The area under the receiver operating characteristic curves, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model in the test set were 0.779, 0.763, 0.883, 0.647, 0.341, and 0.922, respectively. According to the Shapley additive explanation values, drinking, smoking, higher levels of triglyceride, and the occurrence of ANC are associated with RAP. CONCLUSION: The XGBoost model shows good performance in predicting RAP, which may help identify high-risk patients.

The reporting of prognostic prediction models for obstetric care was poor: a cross-sectional survey of 10-year publications.

BACKGROUND: To investigate the reporting of prognostic prediction model studies in obstetric care through a cross-sectional survey design. METHODS: PubMed was searched to identify prognostic prediction model studies in obstetric care published from January 2011 to December 2020. The quality of reporting was assessed by the TRIPOD checklist. The overall adherence by study and the adherence by item were calculated separately, and linear regression analysis was conducted to explore the association between overall adherence and prespecified study characteristics. RESULTS: A total of 121 studies were included, while no study completely adhered to the TRIPOD. The results showed that the overall adherence was poor (median 46.4%), and no significant improvement was observed after the release of the TRIPOD (43.9 to 46.7%). Studies including both model development and external validation had higher reporting quality versus those including model development only (68.1% vs. 44.8%). Among the 37 items required by the TRIPOD, 10 items were reported adequately with an adherence rate over of 80%, and the remaining 27 items had an adherence rate ranging from 2.5 to 79.3%. In addition, 11 items had a report rate lower than 25.0% and even covered key methodological aspects, including blinding assessment of predictors (2.5%), methods for model-building procedures (4.5%) and predictor handling (13.5%), how to use the model (13.5%), and presentation of model performance (14.4%). CONCLUSIONS: In a 10-year span, prognostic prediction studies in obstetric care continued to be poorly reported and did not improve even after the release of the TRIPOD checklist. Substantial efforts are warranted to improve the reporting of obstetric prognostic prediction models, particularly those that adhere to the TRIPOD checklist are highly desirable.

Data source profile reporting by studies that use routinely collected health data to explore the effects of drug treatment.

BACKGROUND: Routinely collected health data (RCD) are important resource for exploring drug treatment effects. Adequate reporting of data source profiles may increase the credibility of evidence generated from these data. This study conducted a systematic literature review to evaluate the reporting characteristics of databases used by RCD studies to explore the effects of drug treatment. METHODS: Observational studies published in 2018 that used RCD to explore the effects of drug treatment were identified by searching PubMed. We categorized eligible reports into two groups by journal impact factor (IF), including the top 5 general medical journals (NEJM, Lancet, JAMA, BMJ and JAMA Internal Medicine) and the other journals. The reporting characteristics of the databases used were described and compared between the two groups and between studies citing and not citing database references. RESULTS: A total of 222 studies were included, of which 53 (23.9%) reported that they applied data linkage, 202 (91.0%) reported the type of database, and 211 (95.0%) reported the coverage of the data source. Only 81 (36.5%) studies reported the timeframe of the database. Studies in high-impact journals were more likely to report that they applied data linkage (65.1% vs. 20.2%) and used electronic medical records (EMR) (73.7% vs. 30.0%) and national data sources (77.8% vs. 51.3%) than those published in other medical journals. There were 137/222 (61.7%) cited database references. Studies with database-specific citations had better reporting of the data sources and were more likely to publish in high-impact journals than those without (mean IF, 6.08 vs. 4.09). CONCLUSIONS: Some deficits were found in the reporting quality of databases in studies that used RCD to explore the effects of drug treatment. Studies citing database-specific references may provide detailed information regarding data source characteristics. The adoption of reporting guidelines and education on their use is urgently needed to promote transparency by research groups.

A systematic evaluation of methodological and reporting quality of meta-analysis published in the field of gastrointestinal endoscopy.

BACKGROUND: To evaluate the methodological and reporting quality of published meta-analyses (MAs) in four major gastrointestinal endoscopic journals, and identify the predicted factors for high quality. METHODS: A systematic search was performed in PubMed to identify MAs from 1, January, 2016 to 31, December, 2020 in four major gastrointestinal endoscopic journals (including Digestive Endoscopy, Gastrointestinal Endoscopy, Surgical Endoscopy, and Endoscopy). We collected the characteristics of MAs after filtering unqualified articles, and assessed methodological and reporting qualities for eligible articles by AMSTAR tool and PRISMA checklist, respectively. Logistic regression was used for identifying predictive factors for high quality. RESULTS: A total of 289 MAs were identified after screening by predefined inclusion and exclusion criteria. The scores (mean ± SD) of AMSTAR and PRISMA were 7.73 ± 1.11 and 22.90 ± 1.85, respectively. In PRISMA checklist, some items had less than 50% complete adherence, including item 2 (structured summary), items 5 (protocol and registration), items 12 and 19 (risk of bias in studies), item 27 (funding support). Item 1 (a priori design), item 4 (gray literature research), item 5 (list of included and excluded) were inferior to 50% adherence in AMSTAR tool. We found the predictive factors for high quality through logistic regression analysis: a priori design and funding support were associated with methodological quality. Protocol and registration influenced the methodological and reporting quality closely. CONCLUSION: In general, qualities on the methodology and the reporting of MAs published in the gastrointestinal endoscopic journals are good, but both of which still potentially need further improvement.

Comparison of clinical characteristics and disease burden between early- and late-onset type 2 diabetes patients: a population-based cohort study.

BACKGROUND: The clinical characteristics of early-onset type 2 diabetes (T2D) patients are not fully understood. To address this gap, we conducted a cohort study to evaluate clinical characteristics and disease burden in the new-onset T2D population, especially regarding the progression of diseases. METHODS: This cohort study was conducted using a population-based database. Patients who were diagnosed with T2D were identified from the database and were classified into early- (age < 40) and late-onset (age ≥ 40) groups. A descriptive analysis was performed to compare clinical characteristics and disease burden between early- and late-onset T2D patients. The progression of disease was compared using Kaplan‒Meier analysis. RESULTS: A total of 652,290 type 2 diabetic patients were included. Of those, 21,347 were early-onset patients, and 300,676 were late-onset patients. Early-onset T2D patients had poorer glycemic control than late-onset T2D patients, especially at the onset of T2D (HbA1c: 9.3 [7.5, 10.9] for early-onset vs. 7.7 [6.8, 9.2] for late-onset, P < 0.001; random blood glucose: 10.9 [8.0, 14.3] for early-onset vs. 8.8 [6.9, 11.8] for late-onset, P < 0.001). Insulin was more often prescribed for early-onset patients (15.2%) than for late-onset patients (14.8%). Hypertension (163.0 [28.0, 611.0] days) and hyperlipidemia (114.0 [19.0, 537.0] days) progressed more rapidly among early-onset patients, while more late-onset patients developed hypertension (72.7% vs. 60.1%, P < 0.001), hyperlipidemia (65.4% vs. 51.0%, P < 0.001), cardiovascular diseases (66.0% vs. 26.7%, P < 0.001) and chronic kidney diseases (5.5% vs. 2.1%, P < 0.001) than early-onset patients. CONCLUSIONS: Our study results indicate that patients with newly diagnosed early-onset T2D had earlier comorbidities of hypertension and hyperlipidemia. Both clinical characteristics and treatment patterns suggest that the degree of metabolic disturbance is more severe in patients with early-onset type 2 diabetes. This highlights the importance of promoting healthy diets or lifestyles to prevent T2D onset in young adults.

[Effects and mechanism of p53 gene deletion on energy metabolism during the pluripotent transformation of spermatogonial stem cells].

Previous studies have shown that long-term spermatogonial stem cells (SSCs) have the potential to spontaneously transform into pluripotent stem cells, which is speculated to be related to the tumorigenesis of testicular germ cells, especially when p53 is deficient in SSCs which shows a significant increase in the spontaneous transformation efficiency. Energy metabolism has been proved to be strongly associated with the maintenance and acquisition of pluripotency. Recently, we compared the difference in chromatin accessibility and gene expression profiles between wild-type (p53+/+) and p53 deficient (p53-/-) mouse SSCs using the Assay for Targeting Accessible-Chromatin with high-throughput sequencing (ATAC-seq) and transcriptome sequencing (RNA-seq) techniques, and revealed that SMAD3 is a key transcription factor in the transformation of SSCs into pluripotent cells. In addition, we also observed significant changes in the expression levels of many genes related to energy metabolism after p53 deletion. To further reveal the role of p53 in the regulation of pluripotency and energy metabolism, this paper explored the effects and mechanism of p53 deletion on energy metabolism during the pluripotent transformation of SSCs. The results of ATAC-seq and RNA-seq from p53+/+ and p53-/- SSCs revealed that gene chromatin accessibility related to positive regulation of glycolysis and electron transfer and ATP synthesis was increased, and the transcription levels of genes encoding key glycolytic enzymes and regulating electron transport-related enzymes were markedly increased. Furthermore, transcription factors SMAD3 and SMAD4 promoted glycolysis and energy homeostasis by binding to the chromatin of the Prkag2 gene which encodes the AMPK subunit. These results suggest that p53 deficiency activates the key enzyme genes of glycolysis in SSCs and enhances the chromatin accessibility of genes associated with glycolysis activation to improve glycolysis activity and promote transformation to pluripotency. Moreover, SMAD3/SMAD4-mediated transcription of the Prkag2 gene ensures the energy demand of cells in the process of pluripotency transformation and maintains cell energy homeostasis by promoting AMPK activity. These results shed light on the importance of the crosstalk between energy metabolism and stem cell pluripotency transformation, which might be helpful for clinical research of gonadal tumors.

Fluid Balance and Ventilator-Associated Events Among Patients Admitted to ICUs in China: A Nested Case-Control Study.

OBJECTIVES: Fluid therapy is an important component of intensive care management, however, optimal fluid management is unknown. The relationship between fluid balance and ventilator-associated events has not been well established. This study investigated the dose-response relationship between fluid balance and ventilator-associated events. DESIGN: Nested case-control study. SETTING: The study was based on a well-established, research-oriented registry of healthcare-associated infections at ICUs of West China Hospital system (Chengdu, China). PATIENTS: A total of 1,528 ventilator-associated event cases with 3,038 matched controls, who consistently underwent mechanical ventilation for at least 4 days from April 1, 2015, to December 31, 2018, were included. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We calculated cumulative fluid balance within 4 days prior to ventilator-associated event occurrence. A weighted Cox proportional hazards model with restricted cubic splines was used to evaluate the dose-response relationship. A nonlinear relationship between fluid balance and all three tiers of ventilator-associated events, patients with fluid balance between -1 and 0 L had the lowest risk (p < 0.05 for nonlinear test). The risk of ventilator-associated event was significantly higher in patients with positive fluid balance (4 d cumulative fluid balance: 1 L: 1.19; 3 L: 1.92; 5 L: 2.58; 7 L: 3.24), but not in those with negative fluid balance (-5 L: 1.34; -3 L: 1.14; -1 L: 0.98). CONCLUSIONS: There was nonlinear relationship between fluid balance and all three tiers of ventilator-associated event, with an fluid balance between -1 and 0 L corresponding to the lowest risk. Positive but not negative fluid balance increased the risk of ventilator-associated events, with higher positive fluid balance more likely to lead to ventilator-associated events.