Summary data-based Mendelian randomization and single-cell RNA sequencing analyses identify immune associations with low-level LGALS9 in sepsis.

Sepsis is one of the major challenges in intensive care units, characterized by the complexity of the host immune status. To gain a deeper understanding of the pathogenesis of sepsis, it is crucial to study the phenotypic changes in immune cells and their underlying molecular mechanisms. We conducted Summary data-based Mendelian randomization analysis by integrating genome-wide association studies data for sepsis with expression quantitative trait locus data, revealing a significant decrease in the expression levels of 17 biomarkers in sepsis patients. Furthermore, based on single-cell RNA sequencing data, we elucidated potential molecular mechanisms at single-cell resolution and identified that LGALS9 inhibition in sepsis patients leads to the activation and differentiation of monocyte and T-cell subtypes. These findings are expected to assist researchers in gaining a more in-depth understanding of the immune dysregulation in sepsis.

A nomogram to predict the risk of proliferative lupus nephritis in patients with systemic lupus erythematosus involving the kidneys.

Proliferative lupus nephritis (PLN) is a serious organ-threatening manifestation of systemic lupus erythematosus (SLE) that is associated with high mortality and renal failure. Here, we analyzed data from 1287 SLE patients with renal manifestations, including 780 of which were confirmed as proliferative or non-proliferative LN patients by renal biopsy, divided into a training cohort (547 patients) and a validation cohort (233 patients). By applying a least absolute shrinkage and selection operator (LASSO) regression approach combined with multivariate logistic regression analysis to build a nomogram for prediction of PLN that was then assessed by receiver operating characteristic (ROC) curves, calibration curves, and clinical decision curves (DCA) in both the training and validation cohorts. The area under the ROC curve (AUC) of the model in the training cohort was 0.921 (95% confidence interval (CI): 0.895-0.946), the AUC of internal validation in the training cohort was 0.909 and the AUC of external validation was 0.848 (95% CI: 0.796-0.900). The nomogram showed good performance as evaluated using calibration and DCA curves. Taken together, our results indicate that our nomogram that comprises 12 significantly relevant variables could be clinically valuable to prognosticate on the risk of PLN in SLE, so as to improve patient prognoses.

Use of statins and risks of ovarian, uterine, and cervical diseases: a cohort study in the UK Biobank.

PURPOSE: To examine the associations between use of statins and risks of various ovarian, uterine, and cervical diseases, including ovarian cancer, endometrial cancer, cervical cancer, ovarian cyst, polycystic ovarian syndrome, endometriosis, endometrial hyperplasia, endometrial polyp, and cervical polyp. METHODS: We conducted a cohort study among female participants in the UK Biobank. Information on the use of statins was collected through verbal interview. Outcome information was obtained by linking to national cancer registry data and hospital inpatient data. We used Cox proportional hazards regression to examine the associations. RESULTS: A total of 180,855 female participants (18,403 statin users and 162,452 non-users) were included. Use of statins was significantly associated with increased risks of cervical cancer (adjusted hazard ratio (HR), 1.55; 95% confidence interval (95% CI), 1.05-2.30) and polycystic ovarian syndrome (adjusted HR, 4.39; 95% CI, 1.68-11.49). However, we observed no significant association between use of statins and risk of ovarian cancer, endometrial cancer, ovarian cyst, endometriosis, endometrial hyperplasia, endometrial polyp, or cervical polyp. CONCLUSION: Our findings suggest that use of statins is associated with increased risks of cervical cancer and polycystic ovarian syndrome, but is not associated with increased or decreased risk of ovarian cancer, endometrial cancer, ovarian cyst, endometriosis, endometrial polyp, or cervical polyp.

MTHFD1L confers a poor prognosis and malignant phenotype in esophageal squamous cell carcinoma by activating the ERK5 signaling pathway.

MTHFD1L, a key enzyme of folate metabolism, is seldom reported in cancer. In this study, we investigate the role of MTHFD1L in the tumorigenicity of esophageal squamous cell carcinoma (ESCC). ESCC tissue microarrays (TMAs) containing 177 samples from 109 patients were utilized to evaluate whether MTHFD1L expression, determined using immunohistochemical analysis, is a prognostic indicator for ESCC patients. The function of MTHFD1L in the migration and invasion of ESCC cells was studied with wound healing, Transwell, and three-dimensional spheroid invasion assays in vitro and a lung metastasis mouse model in vivo. The mRNA microarrays and Ingenuity pathway analysis (IPA) were used to explore the downstream of MTHFD1L. Elevated expression of MTHFD1L in ESCC tissues was significantly associated with poor differentiation and prognosis. These phenotypic assays revealed that MTHFD1L significantly promote the viability and metastasis of ESCC cell in vivo and in vitro. Further detailed analyses of the molecular mechanism demonstrated that the ESCC progression driven by MTHFD1L was through up-regulation ERK5 signaling pathways. These findings reveal that MTHFD1L is positively associated with the aggressive phenotype of ESCC by activating ERK5 signaling pathways, suggesting that MTHFD1L is a new biomarker and a potential molecular therapeutic target for ESCC.

piRBase: integrating piRNA annotation in all aspects.

Piwi-interacting RNAs are a type of small noncoding RNA that have various functions. piRBase is a manually curated resource focused on assisting piRNA functional analysis. piRBase release v3.0 is committed to providing more comprehensive piRNA related information. The latest release covers >181 million unique piRNA sequences, including 440 datasets from 44 species. More disease-related piRNAs and piRNA targets have been collected and displayed. The regulatory relationships between piRNAs and targets have been visualized. In addition to the reuse and expansion of the content in the previous version, the latest version has additional new content, including gold standard piRNA sets, piRNA clusters, piRNA variants, splicing-junction piRNAs, and piRNA expression data. In addition, the entire web interface has been redesigned to provide a better experience for users. piRBase release v3.0 is free to access, browse, search, and download at http://bigdata.ibp.ac.cn/piRBase.

Nanomedicine for urologic cancers: diagnosis and management.

Urologic cancers accounted for more than two million new cases and around 0.8 million deaths in 2020. Although surgery, chemotherapy, and radiotherapy, as well as castration for prostate cancer, remain the cornerstones for managing urologic neoplasms, they can result in severe adverse effects, poor patient compliance, and unsatisfactory survival rates, thus, it is essential to develop novel options that enable the early detection of these malignancies, together with providing accurate diagnoses, and more efficient treatment strategies. Nanomedicine represents an emerging approach that can deliver formulations or drugs across traditional biological barriers in the body and be directed to specific cell types within target organs via active targeting or passive targeting, thus, showing potential to improve the management of urologic cancers. In this review, we discussed the most recent updates on the application of nanomedicines in the diagnosis and treatment of urologic cancers, with focus on prostate, bladder and kidney tumors. We also presented the anti-tumor molecular mechanisms of newly designed nanomedicine for treating urologic cancers, mainly including image-guided surgery, chemotherapy, radiotherapy, gene therapy, immunotherapy, and their synergetic therapy. Current studies have demonstrated the potential advantages of nanomedicine over conventional approaches. However, most developments and new findings in this area have not been validated in clinical trials yet, and therefore, efforts shall be made to translate these research insights into clinical practices for urologic cancers.

The development and progress of nanomedicine for esophageal cancer diagnosis and treatment.

Esophageal cancer (EC) is a common gastrointestinal malignancy with poor prognosis and high mortality. Although combined therapeutic strategies have been developed, the 5-year survival rate of patients with EC remains relatively poor. Conventional anti-cancer drug delivery techniques have some shortcomings, such as nontargeted delivery and nonspecific toxicity. Nanoparticles (NPs) provide a promising platform for delivering drugs in various therapeutic modalities for EC, which possess several remarkable advantages in cancer therapy, such as reduced side effects, prolonged circulation time, and preferential accumulation at the tumor site. In this review, we summarized various types of NPs applied in the treatment of EC, including polymers, micelles, liposomes, inorganic NPs and organic NPs. Meanwhile, we discussed the efficacy and safety of newly designed nanomedicine in various treatments of EC, including chemotherapy, radiotherapy, gene therapy, photodynamic therapy (PDT), photothermal therapy (PTT), and their synergetic therapy. In addition, nanomedicine applied in tumor imaging and diagnoses were also reviewed. Current studies have suggested the potential advantages of nanoformulations over conventional formulations. More researches to promote clinical translation of nanomedicine for EC are anticipated in the future.

Longer Time Interval from Neoadjuvant Chemoradiation to Surgery is Associated with Poor Survival for Patients Without Clinical Complete Response in Oesophageal Cancer.

BACKGROUND: The optimal interval between neoadjuvant therapy and oesophagectomy for oesophageal cancer remains controversial. METHODS: Patients with locally advanced oesophageal squamous cell carcinoma (ESCC) who received neoadjuvant chemoradiotherapy followed by oesophagectomy between June 2017 and December 2020 were prospectively enrolled and retrospectively analysed. Patients were divided into two groups: timely (group A; < 10 weeks) and delayed (group B; ≥ 10 weeks) surgery groups. Survival was the primary outcome, and tumour response and post-operative complications were the secondary outcomes. RESULTS: Overall, 224 patients were recruited; 116 patients (51.8%) underwent timely surgery within 10 weeks (group A), and 108 patients (49.2%) underwent delayed surgery over 10 weeks (group B) after chemoradiotherapy. In patients with clinical complete response (cCR), two groups had no significant difference of survival benefit (P = 0.618). However, in patients without cCR, delayed surgery was associated with poor survival (P = 0.035) and cancer progression (P = 0.036). A total of 40 patients (34.5%) in group A and 54 patients (50.0%) in group B achieved pCR (P = 0.019). pCR rates were significantly different across the four groups and increased over time (P = 0.006). CONCLUSIONS: Patients with a prolonged time interval from neoadjuvant chemoradiation to surgery had higher pCR rates. For patients with cCR to neoadjuvant chemoradiation, the time interval to surgery can be safely prolonged for at least 10 weeks. However, for patients with non-cCR to neoadjuvant chemoradiation, delayed surgery is associated with poor survival, and surgery should be performed within 10 weeks of neoadjuvant chemoradiation.

Real-time prediction of organ failures in patients with acute pancreatitis using longitudinal irregular data.

It is extremely important to identify patients with acute pancreatitis who are at high risk for developing persistent organ failures early in the course of the disease. Due to the irregularity of longitudinal data and the poor interpretability of complex models, many models used to identify acute pancreatitis patients with a high risk of organ failure tended to rely on simple statistical models and limited their application to the early stages of patient admission. With the success of recurrent neural networks in modeling longitudinal medical data and the development of interpretable algorithms, these problems can be well addressed. In this study, we developed a novel model named Multi-task and Time-aware Gated Recurrent Unit RNN (MT-GRU) to directly predict organ failure in patients with acute pancreatitis based on irregular medical EMR data. Our proposed end-to-end multi-task model achieved significantly better performance compared to two-stage models. In addition, our model not only provided an accurate early warning of organ failure for patients throughout their hospital stay, but also demonstrated individual and population-level important variables, allowing physicians to understand the scientific basis of the model for decision-making. By providing early warning of the risk of organ failure, our proposed model is expected to assist physicians in improving outcomes for patients with acute pancreatitis.

Middle-size molecule clearance as measured by ß2-microglobulin in high-flux versus low-flux dialysis and hemodiafiltration: A prospective randomized controlled trial.

BACKGROUND: Whereas most studies to date have mainly concentrated on the comparison between high-flux hemodialysis (HFHD) and hemodiafiltration (HDF), or HFHD and low-flux hemodialysis (LFHD) in relation to the clearance of ß2-microglobulin (ß2M) in HD patients, there have been few related to combined HFHD and HDF therapy. To compare the clearance of middle-sized molecules as measured by ß2M in HFHD versus LFHD and HDF. METHODS: A prospective, single-center, open-label, observer-blinded, randomized controlled trial was conducted at the West China Hospital of Sichuan University in China. Patients received either HFHD or LFHD and HDF 3 times a week with follow-ups at one and 3 months. The primary endpoint was the clearance of ß2M at 3 months. The secondary endpoints included hemodialysis-related adverse events, changes in anemia, states of nutrition, and inflammatory indices. RESULTS: After 3 months of treatment, the HFHD+HDF group achieved a higher satisfaction level than the LFHD+HDF group, with decreased serum ß2M concentrations (34.493 ± 7.257 vs. 43.593 ± 9.036 mg/L, p < 0.001) and elevated red blood cell counts (3.959 ± 0.742 vs. 3.602 ± 0.578 × 1012 /L, p = 0.015). Compared with baseline, both kinds of treatment led to increases in serum urea (t = -3.623, p = 0.001 vs. t = -4.240, p < 0.001), cholesterol (t = -2.511, p = 0.016 vs. t = -4.472, p < 0.001), and magnesium (t = -2.648, p = 0.011 vs. t = -3.561, p = 0.001). An elevated level of serum albumin (t = -2.683, p = 0.010) was observed only in the HFHD+HDF group. CONCLUSIONS: Combined therapy with HFHD and HDF has a beneficial effect on improving ß2M clearance, red blood cell management, and nutrition status in HD patients.

Responses of Rice and Related Cadmium Transporter Genes to the Passivating Microbial Agent MBLHAP.

Alkali-producing microorganisms and hydroxyapatite (HAP), a chemical passivation agent, have a certain remediation effect on cadmium (Cd) -contaminated soil. They can decrease the available Cd content in the soil to a certain extent and reduce the overall Cd content of rice planted in the soil. The Cd-contaminated soil was treated with the passivating bacterial agent that had been developed. Changes in the Cd concentration of rice leaves and soil were observed. Real-time PCR was used to analyse the expression levels of Cd transport protein genes in rice. Then, we determined the activities of super-oxide dismutase (SOD), catalase (CAT) and peroxidase (POD) at different stages of rice growth. The results showed that after HAP, alkali-producing microorganisms and passivating microbial agents were applied to the Cd treated soil. The total Cd content in rice leaves was reduced by 66.80%, 80.32% and 81.35%. The expression differences of genes related to Cd transporter proteins were measured, and the results showed that the changes in gene regulation were consistent with the changes in Cd content of rice leaves. The changes in SOD activity, CAT activity and POD activity further indicated that the three enzymes could alleviate the adverse effects of Cd stress by regulating the related enzyme activities in rice. In conclusion, alkali-producing microorganisms, HAP and passivating bacterial agents can effectively reduce the toxicity of Cd to rice, and reduce the absorption and accumulation of Cd in rice leaves.

HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine.

Pharmacotranscriptomics has become a powerful approach for evaluating the therapeutic efficacy of drugs and discovering new drug targets. Recently, studies of traditional Chinese medicine (TCM) have increasingly turned to high-throughput transcriptomic screens for molecular effects of herbs/ingredients. And numerous studies have examined gene targets for herbs/ingredients, and link herbs/ingredients to various modern diseases. However, there is currently no systematic database organizing these data for TCM. Therefore, we built HERB, a high-throughput experiment- and reference-guided database of TCM, with its Chinese name as BenCaoZuJian. We re-analyzed 6164 gene expression profiles from 1037 high-throughput experiments evaluating TCM herbs/ingredients, and generated connections between TCM herbs/ingredients and 2837 modern drugs by mapping the comprehensive pharmacotranscriptomics dataset in HERB to CMap, the largest such dataset for modern drugs. Moreover, we manually curated 1241 gene targets and 494 modern diseases for 473 herbs/ingredients from 1966 references published recently, and cross-referenced this novel information to databases containing such data for drugs. Together with database mining and statistical inference, we linked 12 933 targets and 28 212 diseases to 7263 herbs and 49 258 ingredients and provided six pairwise relationships among them in HERB. In summary, HERB will intensively support the modernization of TCM and guide rational modern drug discovery efforts. And it is accessible through http://herb.ac.cn/.

[Effect of Short-Term Exposure to Air Pollutants on Hospital Admissions for End-Stage Renal Disease Patients Undergoing Hemodialysis]. / çŸ­æœŸç©ºæ°”æ±¡æŸ“ç‰©æš´éœ²å¯¹ç»ˆæœ«æœŸè‚¾è„ç— è¡€æ¶²é€æžæ‚£è€ å ¥é™¢æ¬¡æ•°çš„å½±å“.

Objective: To evaluate the association between short-term exposure to air pollutants of end-stage renal disease (ESRD) patients on maintenance hemodialysis and the number of daily hospital admissions. Methods: The data on hospitalizations were obtained from the database of the municipal Urban Employees' Basic Medical Insurance and Urban Residents' Basic Medical Insurance of a city in Southwest China. Single and multiple pollutant generalized additive models were utilized to estimate the effect of air pollutants (CO, NO2, O3, PM10, PM2.5, and SO2) on patient admissions after the lag time of different numbers of days. In addition, subgroup analyses stratified by sex, age, PM2.5 and PM10 concentration thresholds, seasonality, and comorbidity status for cardiovascular diseases and hypertension were conducted. Results: In the single pollutant models, the pollutants significantly associated with patient admissions and the corresponding lag time of the strongest association were as follows, every time CO increased by 0.1 mg/m3, there was a 2.39% increase (95% confidence interval [CI]: 0.96%-3.83%) in patient admissions after 7 days of lag time; every time NO2, O3, PM2.5, PM10, and SO2 increased by 10 µg/m3, patient admissions increased by 4.02% (95% CI: 1.21%-6.91%) after 7 days of lag time, 3.57% (95% CI: 0.78%-6.44%) after 0-4 days of lag time, 2.00% (95% CI: 1.07%-2.93%) after 6 days of lag time, 1.19% (95% CI: 0.51%-1.88%) after 7 days of lag time, and 8.37% (95% CI: 3.08%-13.93%) after 7 days of lag time, respectively. In the multiple pollutant model, every time O3 and PM2.5 increased by 10 µg/m3, there was an increase of 3.18% (95% CI: 0.34%-6.09%) in daily patient admissions after 0-4 days of lag time and an increase of 1.85% (95% CI: 0.44%-3.28%) after 7 days of lag time. Furthermore, subgroup analyses showed that seasonality, the severity of air pollution, and patients' comorbidities might be the effect modifiers for the association between ambient air pollution and hospital admissions in ESRD patients receiving maintenance hemodialysis. Conclusion: Air pollution is closely associated with hospital admissions in ESRD patients undergoing maintenance hemodialysis and the strength of this association varies according to seasonality, the severity of air pollution, and patients' status of comorbidities.

Overexpressed COL3A1 has prognostic value in human esophageal squamous cell carcinoma and promotes the aggressiveness of esophageal squamous cell carcinoma by activating the NF-κB pathway.

Alpha-1 Type Ⅲ Collagen (COL3A1) encodes the Collagen alpha-1(Ⅲ) chain, which is a fibrillar collagen that exists in extensile connective tissues. Few studies have reported its role in tumorigenicity. In the present study, we identified that COL3A1 protein and mRNA expression levels were considerably up-regulated in esophageal squamous cell carcinoma (ESCC) cells in comparison with normal esophageal squamous epithelial cells (P < 0.05). Immunohistochemical (IHC) analysis of 114 paraffin-embedded archived ESCC tissues demonstrated that COL3A1 expression was positively correlated with the postoperative T stage. Univariate and multivariable analysis demonstrated that COL3A1 expression was an independent poor prognostic factor for overall survival in the whole cohort. Silencing COL3A1 inhibited, while overexpressing COL3A1 promoted, the proliferation, migration, and invasion of ESCC cells. Furthermore, down-regulation of COL3A1 expression also suppressed the growth of ESCC in subcutaneous xenograft mouse models and inhibited ESCC metastasis in lung metastasis mouse models. In addition, we proved that the tumor-promoting effect of COL3A1 on ESCC cells was related to the activation of NF-κB signaling pathway. These findings indicate that COL3A1 confers a poor prognosis and malignant phenotype by activating the NF-κB pathway in ESCC, potentially representing a novel biomarker and/or providing a new curative target for ESCC.

Acid-Responsive Aggregated Gold Nanoparticles for Radiosensitization and Synergistic Chemoradiotherapy in the Treatment of Esophageal Cancer.

Radiotherapy and chemotherapy are limited by insufficient therapeutic efficacy of low-dose radiation and nonspecific drug biodistribution. Herein, an acid-responsive aggregated nanosystem (AuNPs-D-P-DA) loaded with doxorubicin (DOX) is designed for radiosensitization and synergistic chemoradiotherapy. In response to the acid microenvironment of esophageal cancer (EC), small-sized AuNPs-D-P-DA forms large-sized gold nanoparticle (AuNPs) aggregates in tumor tissues to hinder the backflow of AuNPs to the circulation, resulting in enhanced tumor accumulation and retention. Simultaneously, the AuNPs-based radiosensitization is significantly improved because of the high concentration and large size of intratumoral AuNPs, while DOX are delivered and released specifically into tumor cells triggered by the acid microenvironment for chemo-radio synergistic therapy. Acid-responsive AuNPs exacerbate radiation-induced DNA damage, cell apoptosis, cell cycle arrest, and low colony formation ability in vitro and enhance anti-tumor efficacy in vivo compared to un-responsive control. When combined with acid-responsive DOX, the therapeutic efficacy of the formulation is further improved by their synergistic effect. After the treatment of acid-responsive AuNPs plus radiotherapy, fatty acid metabolism is reprogrammed in xenograft models, which provides potential targets for further improvement of radiosensitization. In summary, the acid-responsive AuNPs-D-P-DA nanosystem leverages the radio- and chemotherapeutic synergies of AuNPs-sensitized X-ray irradiation and acid-responsive DOX in the treatment of EC.

The emerging role of long noncoding RNAs in esophageal carcinoma: from underlying mechanisms to clinical implications.

Long noncoding RNAs (lncRNAs), a type of transcriptional product more than 200 nucleotides in length, have emerged as crucial regulators in human cancers. Accumulating data have recently indicated relationships between lncRNAs and esophageal carcinoma (EC). Of note, lncRNAs act as decoys/sponges, scaffolds, guides, and signals to regulate the expression of oncogenes or tumor suppressors at epigenetic, post-transcriptional, and protein levels, through which they exert their unique EC-driving or EC-suppressive functions. Moreover, the features of EC-related lncRNAs have been gradually exploited for developing novel diagnostic and therapeutic strategies in clinical scenarios. LncRNAs have the potential to be used as diagnostic and prognostic indicators individually or in combination with other clinical variables. Beyond these, although the time is not yet ripe, therapeutically targeting EC-related lncRNAs via gene editing, antisense oligonucleotides, RNA interference, and small molecules is likely one of the most promising therapeutic strategies for the next generation of cancer treatment. Herein, we focus on summarizing EC-driving/suppressive lncRNAs, as well as discussing their different features regarding expression profiles, modes of action, and oncological effects. Moreover, we further discuss current challenges and future developing possibilities of capitalizing on lncRNAs for EC early diagnosis and treatment.

Relationship between modifiable lifestyle factors and chronic kidney disease: a bibliometric analysis of top-cited publications from 2011 to 2020.

BACKGROUND: Chronic kidney disease (CKD) affects 8 to 16% of the world's population and is one of the top ten important drivers of increasing disease burden. Apart from genetic predisposition, lifestyle factors greatly contribute to the incidence and progression of CKD. The current bibliometric analysis aims to characterize the current focus and emerging trends of the research about the impact of modifiable lifestyle factors on CKD. METHODS: We searched articles addressing the impact of modifiable lifestyle factors on the incidence and/or progression of CKD, published between 2011 and 2020, from the Science Citation Index Expanded (SCIE) database. An adjusted citation index, which considered both the original citation count and publication year, was derived for the selection of most-cited publications. Publishing trends, co-authorship network, keywords, and research hotspots were analyzed and visualized. RESULTS: Among the top 100 most influential articles, 32 were narrative reviews, 16 systematic reviews and/or meta-analysis, 44 clinical research, and 8 basic research. The United States occupied a dominant position in the perspective of article numbers and international partnerships, followed by European countries. The modifiable factors that drew the most and constant attention over the decade were diet or nutrition management reported in 63 papers, followed by obesity or body mass index (n = 27), and physical activity or exercises (n = 8). Alcohol consumption, fish oil, chain fatty-acids, and water-soluble vitamins were emerging hotspots identified in the recent most cited publications. CONCLUSIONS: Based on the bibliometric analysis of the most influential articles, our study provides a comprehensive description of publishing trends and research focus over a decade in the field of lifestyle factors' impact on CKD. Diet, obesity, and physical activity were factors receiving the most attention in this topic.

LightGBM: an efficient and accurate method for predicting pregnancy diseases.

As machine learning is becoming the fashion in disease prediction while no prediction model has performed very efficiently and accurately on predicting pregnancy diseases up to now, it's necessary to compare several common machine learning methods' performance on pregnancy diseases prediction and select out the best one. The data of two common pregnancy complications, pregnancy-induced hypertension (PIH) and Intrahepatic cholestasis of pregnancy (ICP), based on various maternal characteristics measured in patients' routine blood examination in 10-19 weeks of gestation are considered to be suitable to be learned. This is a retrospective study of 320 healthy pregnancies in 10-19 weeks, with 149 patients who subsequently developed PIH and 250 patients who subsequently developed ICP. Nine machine learning methods were used to predict PIH and ICP and their performance was compared via 8 evaluation indexes. Finally, the light Gradient Boosting Machine (lightGBM) is considered to be the best method to predict gestational diseases.Impact statementWhat is already known on this subject? As a kind of commonly used method in disease prediction, machine learning could be applied to clinical data for developing robust risk models and many achievements have been made. Also, machine learning can be used to predict pregnancy diseases. Although some machine learning methods have been used for screening gestational diseases, methods based on simple theories, such as logistic regression and decision tree, are frequently used. They don't always have a very satisfactory prediction results. Besides, only a few types of pregnancy diseases can be predicted.What do the results of this study add? LightGBM has the best prediction results of PIH and ICP among 9 machine learning methods in this study. It can predict PIH (AUC = 81.72%) with a sensitivity of 70.59%, and ICP (AUC = 95.91%) with a sensitivity of 97.91%.What are the implications of these findings for clinical practice and/or further research? A new model has been developed for effective first-trimester screening for two common pregnancy diseases, PIH and ICP. This lightGBM model can be used in relative hospitals and population of the research, and provide references for doctors' diagnosis and treatment of pregnant women. In further research, the predicted effect of lightGBM on daily practice and other pregnancy diseases such as pregnancy diabetes, will be verified.

The Efficacy of Neoadjuvant Versus Adjuvant Therapy for Resectable Esophageal Cancer Patients: A Systematic Review and Meta-Analysis.

OBJECTIVE: Inconclusive results are available as to whether chemo/radiotherapy should be administered to resectable esophageal cancer patients before surgery (neoadjuvant therapy) or after surgery (adjuvant therapy). The paper, via a meta-analysis of effects of treatment modalities when administering chemo/radiotherapy, aims to systematically evaluate the effect of timing of chemo/radiotherapy and surgery. METHODS: We performed a systematic literature search for clinical trials of neoadjuvant and adjuvant therapy for patients with esophageal cancer. Using meta-analysis, we conducted direct and adjusted indirect comparisons of overall survival, complete resection rate (R0 resection), perioperative mortality, leakage rate and local recurrence in patients with resectable esophageal cancer. RESULTS: A total of 32 studies involving 7985 patients with esophageal cancer were included in the meta-analysis. Twenty-five randomized controlled studies indirectly compared neoadjuvant/adjuvant therapy with surgery alone, while five non-randomized controlled studies and two randomized controlled studies directly compared neoadjuvant with adjuvant therapy. Neoadjuvant therapy followed by surgery, compared with surgery along with adjuvant therapy, showed a significant overall survival advantage in our pooled analysis (HR 0.88; 95% CI 0.79-0.98). Directly compared with adjuvant therapy, neoadjuvant therapy demonstrated a lower local recurrence rate (OR 0.56; 95% CI 0.43-0.74) with low heterogeneity (I2 = 1%). Neoadjuvant therapy, comparing to surgery with or without adjuvant therapy, showed a significantly higher R0 resection rate (OR 2.86; 95% CI 2.02-4.04) with moderate heterogeneity (I2 = 38%) and no significant differences in postoperative anastomotic leakage (P = 0.50). However, neoadjuvant therapy, compared with surgery adjuvant therapy, significantly increased perioperative mortality in both direct and indirect comparisons (P < 0.01). CONCLUSIONS: We found that neoadjuvant therapy was associated with higher overall survival and R0 resection rate without increasing postoperative anastomotic leakage for patients with resectable esophageal cancer, whereas neoadjuvant therapy was associated with higher perioperative mortality after esophagectomy.

A network-based variable selection approach for identification of modules and biomarker genes associated with end-stage kidney disease.

AIMS: Intervention for end-stage kidney disease (ESKD), which is associated with adverse prognoses and major economic burdens, is challenging due to its complex pathogenesis. The study was performed to identify biomarker genes and molecular mechanisms for ESKD by bioinformatics approach. METHODS: Using the Gene Expression Omnibus dataset GSE37171, this study identified pathways and genomic biomarkers associated with ESKD via a multi-stage knowledge discovery process, including identification of modules of genes by weighted gene co-expression network analysis, discovery of important involved pathways by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, selection of differentially expressed genes by the empirical Bayes method, and screening biomarker genes by the least absolute shrinkage and selection operator (Lasso) logistic regression. The results were validated using GSE70528, an independent testing dataset. RESULTS: Three clinically important gene modules associated with ESKD, were identified by weighted gene co-expression network analysis. Within these modules, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed important biological pathways involved in ESKD, including transforming growth factor-ß and Wnt signalling, RNA-splicing, autophagy and chromatin and histone modification. Furthermore, Lasso logistic regression was conducted to identify five final genes, namely, CNOT8, MST4, PPP2CB, PCSK7 and RBBP4 that are differentially expressed and associated with ESKD. The accuracy of the final model in distinguishing the ESKD cases and controls was 96.8% and 91.7% in the training and validation datasets, respectively. CONCLUSION: Network-based variable selection approaches can identify biological pathways and biomarker genes associated with ESKD. The findings may inform more in-depth follow-up research and effective therapy.