Exosomal transfer of HCC-derived miR-17-5p downregulates NK cell function by targeting RUNX1-NKG2D axis.

OBJECTIVE: Natural killer (NK) cells are an integral part of the staunch defense line against malignant tumors within the tumor microenvironment. Existing research indicates that miRNAs can influence the development of NK cells by negatively modulating gene expression. In this study, we aim to explore how the miR-17-5p in Hepatocellular Carcinoma (HCC) exosomes regulates the killing function of NK cells towards HCC cells through the transcription factor RNX1. METHODS: The exosomes were isolated from HCC tissues and cell lines, followed by a second generation sequencing to compare differential miRNAs. Verification was performed using qRT-PCR and Western blot methods. The mutual interactions between miR-17-5p and RUNX1, as well as between RUNX1 and NKG2D, were authenticated using techniques like luciferase reporter gene assays, Western blotting, and Chromatin Immunoprecipitation (ChIP). The cytotoxic activity of NK cells towards HCC cells in vitro was measured using methods such as RTCA and ELISPOT. The zebrafish xenotransplantation was utilized to assess the in vivo killing capacity of NK cells against HCC cells. RESULTS: The level of miR-17-5p in exosomes from HCC tissue increased compared to adjacent tissues. We verified that RUNX1 was a target of miR-17-5p and that RUNX1 enhances the transcription of NKG2D. MiR-17-5p was found to downregulate the expression of RUNX1 and NKG2D, subsequently reducing the in vitro and in vivo cytotoxic capabilities of NK cells against HCC cells. CONCLUSIONS: The miR-17-5p found within HCC exosomes can target RUNX1, subsequently attenuating the cytotoxic activity of NK cells.

A patient-specific lung cancer assembloid model with heterogeneous tumor microenvironments.

Cancer models play critical roles in basic cancer research and precision medicine. However, current in vitro cancer models are limited by their inability to mimic the three-dimensional architecture and heterogeneous tumor microenvironments (TME) of in vivo tumors. Here, we develop an innovative patient-specific lung cancer assembloid (LCA) model by using droplet microfluidic technology based on a microinjection strategy. This method enables precise manipulation of clinical microsamples and rapid generation of LCAs with good intra-batch consistency in size and cell composition by evenly encapsulating patient tumor-derived TME cells and lung cancer organoids inside microgels. LCAs recapitulate the inter- and intratumoral heterogeneity, TME cellular diversity, and genomic and transcriptomic landscape of their parental tumors. LCA model could reconstruct the functional heterogeneity of cancer-associated fibroblasts and reflect the influence of TME on drug responses compared to cancer organoids. Notably, LCAs accurately replicate the clinical outcomes of patients, suggesting the potential of the LCA model to predict personalized treatments. Collectively, our studies provide a valuable method for precisely fabricating cancer assembloids and a promising LCA model for cancer research and personalized medicine.

[Characteristics of Ozone Concentration in Shanghai and Its Associated Atmospheric Circulation Background During Summer Half-years from 2006 to 2021].

It is of great importance to scientifically evaluate the impact of weather and climate conditions on the occurrence of O3 pollution in order to improve the accuracy of O3 pollution forecasts， as well as to reasonably control and reduce the adverse effects of O3 pollution. The characteristics of O3 concentration and climate background were analyzed based on daily O3 concentration data， meteorological factors， and NCEP/NCER reanalysis data from 2006 to 2021 in Shanghai. In addition， the differences in atmospheric circulation situations during years with anomalous O3 concentrations were compared and diagnosed from the perspective of climatology. Additionally， the monthly O3 concentration prediction model （seasonal autoregressive integrated moving average with exogenous regressors， SARIMAX） was further established by adding the key meteorological factors. The results indicated that both the whole-year average and summer half-year average O3 concentrations in Shanghai were increasing with fluctuation， and the summer half-year average was much higher than the annual average， up to 36.2%. Furthermore， there was a significant negative correlation between O3 concentration and wind speed （correlation coefficient of -0.826） and a significant positive correlation with the frequency of static wind and the number of days in which the low cloud cover was less than 20% （correlation coefficients of 0.836 and 0.724， respectively）. The monthly mean O3 concentration had a clear periodicity， showing a pattern with a high concentration in the middle period （April to September） and a low concentration at the beginning and end of the periods. High O3 concentration years （2013-2021） were accompanied by more polluted days， lower average wind speed， more small wind （≤1.5 m·s-1） days， more days of low cloud cover of less than 20%， more days of high temperature， higher direct solar radiation， and more sunshine hours. When the location of the stronger West Pacific subtropical high was westward and southward in the summer half-year， Shanghai was influenced by an anomalous westerly wind， which was not conducive to the transportation of clean air from the sea to Shanghai and thus led to the high concentration of O3 pollution. When the long wave radiation emitted from the ground was low in the summer half-year， it was favorable for the increase in ground temperature and caused a high concentration of O3 pollution. Adding direct solar radiation， maximum temperature， and wind speed as exogenous variables to the monthly O3 forecast model could significantly improve the effectiveness of the monthly forecast， with the root mean square error decreasing by 47.7% （from 22 to 11.5） and the correlation coefficient increasing by 11.2% （from 0.819 to 0.911）， which could be applied to the practical prediction of monthly O3 concentration.

Effectiveness and safety of camrelizumab-containing neoadjuvant therapy in patients with esophageal squamous cell carcinoma: a prospective multicenter observational cohort study.

Background: Camrelizumab has been demonstrated to be a feasible treatment option for locally advanced esophageal squamous cell carcinoma (ESCC) when combined with neoadjuvant chemotherapy. This trial was conducted to investigate the effectiveness and safety of camrelizumab-containing neoadjuvant therapy in patients with ESCC in daily practice. Methods: This prospective multicenter observational cohort study was conducted at 13 tertiary hospitals in Southeast China. Patients with histologically or cytologically confirmed ESCC [clinical tumor-node-metastasis (cTNM) stage I-IVA] who had received at least one dose of camrelizumab-containing neoadjuvant therapy were eligible for inclusion. Results: Between June 1, 2020 and July 13, 2022, 255 patients were enrolled and included. The median age was 64 (range, 27 to 82) years. Most participants were male (82.0%) and had clinical stage III-IVA diseases (82.4%). A total of 169 (66.3%) participants underwent surgical resection; 146 (86.4%) achieved R0 resection, and 36 (21.3%) achieved pathological complete response (pCR). Grades 3-5 adverse events (AEs) were experienced by 14.5% of participants. Reactive cutaneous capillary endothelial proliferation occurred in 100 (39.2%) of participants and all were grade 1 or 2. Conclusions: Camrelizumab-containing neoadjuvant therapy has acceptable effectiveness and safety profiles in real-life ESCC patients.

Is laparoscope surgery feasible for upper gastrointestinal cancer patients with a history of abdominal surgery?

Objective: To investigate the feasibility of laparoscopic abdominal mobilization in patients with cancers of the esophagus or gastroesophageal junction who have a history of abdominal surgery. Methods: A total of 132 patients who underwent resection for cancers of the esophagus or gastroesophageal junction from August 2018 to March 2022 in the Department of Thoracic Surgery, Cancer Hospital, Chinese Academy of Medical Sciences, were selected (66 patients with a history of abdominal surgery (observation group) and 66 patients without a history of abdominal surgery (control group)). All patients were treated with preoperative neoadjuvant therapy, based on the clinical stage. Thoracoscopic and laparoscopic resection was performed under general anesthesia. The intraoperative and postoperative conditions and surgical complications were compared between the two groups. Results: No significant differences were found in baseline data between the observation group and the control group (p > 0.05). Laparoscopic abdominal mobilization was completed in both groups, and there were no significant differences between the two groups in the total operation time [(272.50 ± 86.45) min vs. (257.55 ± 67.96) min], abdominal mobilization time [(25.03 ± 9.82) min vs. (22.53 ± 3.88) min], blood loss [(119.09 ± 72.17) ml vs. (104.39 ± 43.82) ml], and postoperative time to first flatus [(3.44 ± 0.73) d vs. (3.29 ± 0.60) d] (p > 0.05). The abdominal mobilization time was longer in observation group than that in control group (p = 0.057). After excluding the patients (31/66) with a history of simple appendectomy from the observation group, the abdominal mobilization time was significantly longer in observation group than that in control group [(27.97 ± 12.16) min vs. (22.53 ± 3.88) min] (p < 0.05). There were significantly fewer dissected abdominal lymph nodes in the observation group than in the control group [(18.44 ± 10.87) vs. (23.09 ± 10.95), p < 0.05]. After excluding the patients (15/66) with a history of abdominal tumor surgery from the observation group, there was no significant difference in the number of dissected abdominal lymph nodes between the two groups [(20.62 ± 10.81) vs. (23.09 ± 10.95)] (p > 0.05).In addition, no postoperative complications, such as intestinal obstruction, abdominal infection and bleeding, occurred in either group. Conclusion: Patients with cancers of the esophagus or gastroesophageal junction who have a history of abdominal surgery are suitable for minimally invasive laparoscopic mobilization.

Explainable survival analysis with uncertainty using convolution-involved vision transformer.

Image-based precision medicine research is able to help doctors make better decisions on treatments. Among all kinds of medical images, a special form is called Whole Slide Image (WSI), which is used for diagnosing patients with cancer, aiming to enable more accurate survival prediction with its high resolution. However, One unique challenge of the WSI-based prediction models is processing the gigabyte-size or even terabyte-size WSIs, which would make most models computationally infeasible. Although existing models mostly use a pre-selected subset of key patches or patch clusters as input, they might discard some important morphology information, making the prediction inferior. Another challenge is improving the prediction models' explainability, which is crucial to help doctors understand the predictions given by the models and make faithful decisions with high confidence. To address the above two challenges, in this work, we propose a novel explainable survival prediction model based on Vision Transformer. Specifically, we adopt dual-channel convolutional layers to utilize the complete WSIs for more accurate predictions. We also introduce the aleatoric uncertainty into our model to understand its limitation and avoid overconfidence in using the prediction results. Additionally, we present a post-hoc explainable method to identify the most salient patches and distinct morphology features as supporting evidence for predictions. Evaluations of two large cancer datasets show that our proposed model is able to make survival predictions more effectively and has better explainability for cancer diagnosis.

The association between air pollution and the daily hospital visits for atrial fibrillation recorded by ECG: a cross-sectional study.

BACKGROUND: The relationship between air pollution and atrial fibrillation (AF) recorded by electrocardiograph (ECG) has not yet been illustrated which worsens AF precaution and treatment. This research evaluated the association between air pollution and daily hospital visits for AF with ECG records. METHODS: The study enrolled 4933 male and 5392 female patients whose ECG reports indicated AF from 2015 to 2018 in our hospital. Such data were then matched with meteorological data, including air pollutant concentrations, collected by local weather stations. A case-crossover study was performed to assess the relationship between air pollutants and daily hospital visits for AF recorded by ECG and to investigate its lag effect. RESULTS: Our analysis revealed statistically significant associations between AF occurrence and demographic data, including age and gender. This effect was stronger in female (k = 0.02635, p < 0.01) and in patients over 65 y (k = 0.04732, p < 0.01). We also observed a hysteretic effect that when exposed to higher nitrogen dioxide(NO2), counting AF cases recorded by ECG may elevate at lag 0 with a maximum odds ratio(OR) of 1.038 (95% CI 1.014-1.063), on the contrary, O3 reduced the risk of daily visits for AF and its maximum OR was at lag 2, and the OR value was 0.9869 (95% CI 0.9791-0.9948). Other air pollutants such as PM2.5, PM10, and SO2 showed no clear relationship with the recorded AF. CONCLUSION: The associations between air pollution and AF recorded with ECG were preliminarily discovered. Short-term exposure to NO2 was significantly associated with daily hospital visits for AF management.

Comparison of neoadjuvant nab-paclitaxel plus immunotherapy versus paclitaxel plus immunotherapy for esophageal squamous cell carcinoma.

BACKGROUND: This study aimed to compare the feasibility of nab-paclitaxel plus platinum-based chemotherapy (nabTP) versus paclitaxel plus platinum-based chemotherapy (TP) with immune checkpoint inhibitors (ICIs) as a neoadjuvant modality for locally resectable esophageal squamous cell carcinoma (ESCC). METHODS: Between April 2019 and March 2022, we identified ESCC patients who received neoadjuvant immunotherapy with both nabTP (n = 213) and TP (n = 98) at our institution and Henan Cancer Hospital. The patients in the ICIs-nabTP and ICIs-TP groups were pair-matched (1:1) for tumor location, sex, smoking, drinking, clinical T and N stage. The primary endpoint was the hazard of 30-day major postoperative complications. Second, logistic models were applied to estimate the risk factors for pathological complete response (pCR) rate. RESULTS: All patients underwent esophagectomy with R0 resection. A statistically significant increase in the risk of developing major pulmonary (odds ratio [OR], 1.182; 95% confidence interval [CI]: 0.530-2.635; p = 0.683), anastomotic (OR, 1.881; 95% CI: 0.607-5.830; p = 0.267), cardiac (OR, 1.000; 95% CI: 0.426-2.349; p = 1.000) complications after neoadjuvant immunotherapy plus nabTP was not observed. The median interval to surgery was 39 days in the ICIs-nabTP group versus 44 days in the ICIs-TP group (p = 0.119). There was no 30-day mortality in each group. However, there was a slight difference in the 30-day readmission rate (p = 0.043) and the incidence of hydropneumothorax (p = 0.027) between the two groups. The pCR rates of the ICIs-nabTP and ICIs-TP group were 36.7 and 21.4%, respectively (p = 0.018). CONCLUSIONS: It appears to be feasible to add immunotherapy to nabTP regimen for locally advanced ESCC. Compared with TP, nabTP plus ICIs can achieve a better pCR rate in ESCC.

Molecular Regulation and Evolution of Cytokinin Signaling in Plant Abiotic Stresses.

The sustainable production of crops faces increasing challenges from global climate change and human activities, which leads to increasing instances of many abiotic stressors to plants. Among the abiotic stressors, drought, salinity and excessive levels of toxic metals cause reductions in global agricultural productivity and serious health risks for humans. Cytokinins (CKs) are key phytohormones functioning in both normal development and stress responses in plants. Here, we summarize the molecular mechanisms on the biosynthesis, metabolism, transport and signaling transduction pathways of CKs. CKs act as negative regulators of both root system architecture plasticity and root sodium exclusion in response to salt stress. The functions of CKs in mineral-toxicity tolerance and their detoxification in plants are reviewed. Comparative genomic analyses were performed to trace the origin, evolution and diversification of the critical regulatory networks linking CK signaling and abiotic stress. We found that the production of CKs and their derivatives, pathways of signal transduction and drought-response root growth regulation are evolutionarily conserved in land plants. In addition, the mechanisms of CK-mediated sodium exclusion under salt stress are suggested for further investigations. In summary, we propose that the manipulation of CK levels and their signaling pathways is important for plant abiotic stress and is, therefore, a potential strategy for meeting the increasing demand for global food production under changing climatic conditions.

Molecular Regulation and Evolution of Redox Homeostasis in Photosynthetic Machinery.

The recent advances in plant biology have significantly improved our understanding of reactive oxygen species (ROS) as signaling molecules in the redox regulation of complex cellular processes. In plants, free radicals and non-radicals are prevalent intra- and inter-cellular ROS, catalyzing complex metabolic processes such as photosynthesis. Photosynthesis homeostasis is maintained by thiol-based systems and antioxidative enzymes, which belong to some of the evolutionarily conserved protein families. The molecular and biological functions of redox regulation in photosynthesis are usually to balance the electron transport chain, photosystem II, photosystem I, mesophyll and bundle sheath signaling, and photo-protection regulating plant growth and productivity. Here, we review the recent progress of ROS signaling in photosynthesis. We present a comprehensive comparative bioinformatic analysis of redox regulation in evolutionary distinct photosynthetic cells. Gene expression, phylogenies, sequence alignments, and 3D protein structures in representative algal and plant species revealed conserved key features including functional domains catalyzing oxidation and reduction reactions. We then discuss the antioxidant-related ROS signaling and important pathways for achieving homeostasis of photosynthesis. Finally, we highlight the importance of plant responses to stress cues and genetic manipulation of disturbed redox status for balanced and enhanced photosynthetic efficiency and plant productivity.

Chemical-Based Surface Plasmon Resonance Imaging of Fingerprints.

Fingerprints are extremely useful in personal identification; however, they are usually based on physical rather than chemical images because it remains a challenge to reveal a clear chemical fingerprint easily and sensitively. Herein, a surface plasmon resonance imaging (SPRi) method, combined with a chemically selective stepwise signal amplification (CS3A) strategy, is proposed to chemically image fingerprints with adjustable sensitivity and clarity. High-fidelity glucose-associated fingerprint images were obtained at five to seven cycles of CS3A based on the recognition reaction of concanavalin A (ConA) with dextran. The method is also extendable to image substances that possess and/or can be tagged with ConA- or dextran-recognizable groups. For demonstration, SPRi of carboxylic substances was conducted by amidating the carboxyl group with glucosamine to enable the ConA-based CS3A. Glucose- and carboxyl-based fingerprints were simultaneously and clearly imaged, allowing us to perform quantitative analysis of the representative of either glucose or amino acid (e.g., serine) or both. The curves measured from the standard spots were linear in the ranges of 1-4000 µM for glucose and 3.2-4000 µM for serine, with linear correlated coefficients of 0.9979 and 0.9962, respectively. It was then applied to the study of metabolic secretions in fingerprints during running exercise, yielding variation tendencies similar to those measured from sweat samples in the literature. As a noninvasive tool, the CS3A-coupled SPRi reveals both clear images of fingerprints and quantitative chemical information, and it is anticipated to become a competitive new method for chemically imaging fingerprints.

High-Precision Intelligent Cancer Diagnosis Method: 2D Raman Figures Combined with Deep Learning.

Raman spectroscopy, as a label-free detection technology, has been widely used in tumor diagnosis. However, most tumor diagnosis procedures utilize multivariate statistical analysis methods for classification, which poses a major bottleneck toward achieving high accuracy. Here, we propose a concept called the two-dimensional (2D) Raman figure combined with convolutional neural network (CNN) to improve the accuracy. Two-dimensional Raman figures can be obtained from four transformation methods: spectral recurrence plot (SRP), spectral Gramian angular field (SGAF), spectral short-time Fourier transform (SSTFT), and spectral Markov transition field (SMTF). Two-dimensional CNN models all yield more than 95% accuracy, which is higher than the PCA-LDA method and the Raman-spectrum-CNN method, indicating that 2D Raman figure inputs combined with CNN may be one reason for gaining excellent performances. Among 2D-CNN models, the main difference is the conversion, where SRP is based on the structure of wavenumber series with the best performances (98.9% accuracy, 99.5% sensitivity, 98.3% specificity), followed by SGAF on the wavenumber series, SSTFT on wavenumber and intensity information, and SMTF on wavenumber position information. The inclusion of external information in the conversion may be another reason for improvement in the accuracy. The excellent capability shows huge potential for tumor diagnosis via 2D Raman figures and may be applied in other spectroscopy analytical fields.

Capillary array electrophoresis imaging of biochemicals in tissue sections.

It is of great significance to reveal the molecular distribution images in biological tissues, which has led to the bloom of mass spectrometry imaging. Unfortunately, its application is encountering the resistance of high technical barriers and equipment cost, as well as the inability to image substances that cannot be desorbed or ionized, or cannot be separated by their mass-to-charge ratios. Herein presented is a complementary and cost-effective method called capillary array electrophoresis (CAE) imaging. To have the information of molecules and their spatial location, a gridding cutter was fabricated to orderly dissect a tissue section into a leakproof array of micro wells enclosed by the grid-blade arrays. After in situ extraction and fluorophore-labeling of analytes, the samples in the wells were directly subjected to CAE-LIF (laser-induced fluorescence), and the molecular distribution images were depicted with the separated peaks. The practicability was demonstrated by CAE imaging of rat brain tissue sections with amino acid neurotransmitters (e.g., glutamine, 4-aminobutyric acid, alanine, glutamic acid and aspartic acid) as targets. The resultant images showed the global differences of molecular distributions, with a spatial resolution of 1000 µm that was presently determined by the well width but ultimately by the bore size of capillary (down to 10-50 µm). CAE imaging can hence be promising for its low cost, low technical barriers and abundant mechanisms to separate the charged and non-charged, chiral and non-chiral substances.

Highly accurate diagnosis of lung adenocarcinoma and squamous cell carcinoma tissues by deep learning.

Intraoperative detection of the marginal tissues is the last and most important step to complete the resection of adenocarcinoma and squamous cell carcinoma. However, the current intraoperative diagnosis is time-consuming and requires numerous steps including staining. In this paper, we present the use of Raman spectroscopy with deep learning to achieve accurate diagnosis with stain-free process. To make the spectrum more suitable for deep learning, we utilize an unusual way of thinking which regards Raman spectral signal as a sequence and then converts it into two-dimensional Raman spectrogram by short-time Fourier transform as input. The normal-adenocarcinoma deep learning model and normal-squamous carcinoma deep learning model both achieve more than 96% accuracy, 95% sensitivity and 98% specificity when test, which higher than the conventional principal components analysis-linear discriminant analysis method with normal-adenocarcinoma model (0.896 accuracy, 0.867 sensitivity, 0.926 specificity) and normal-squamous carcinoma model (0.821 accuracy, 0.776 sensitivity, 1.000 specificity). The high performance of deep learning models provides a reliable way for intraoperative detection of marginal tissue, and is expected to reduce the detection time and save human lives.

Comparative genomic analysis of esophageal squamous cell carcinoma among different geographic regions.

Introduction: Esophageal squamous cell carcinoma (ESCC) shows remarkable variation in incidence, survival, and risk factors. Although the genomic characteristics of ESCC have been extensively characterized, the genomic differences between different geographic regions remain unclear. Methods: In this study, we sequenced 111 patients with ESCC from northern (NC) and southern (SC) China, combined their data with those of 1081 cases from previous reports, and performed a comparative analysis among different regions. In total, 644 ESCC cases were collected from six geographic regions (NC, SC, Xinjiang, China [XJC], Japan [JP], Vietnam [VN], and Europe & America [EA]) as the discovery cohort. Validation cohort 1 included 437 patients with ESCC from the NC region. Validation cohort 2 included 54 and 57 patients from the NC and SC regions, respectively. Results: Patients with ESCC in different regions had different genomic characteristics, including DNA signatures, tumor mutation burdens, significantly mutated genes (SMGs), altered signaling pathways, and genes associated with clinical features. Based on both the DNA mutation signature and the mutation profile of the most common genes, the NC and SC groups were clustered close together, followed by the JP, XJC, EA, and VN groups. Compared to patients with ESCC from SC, SMGs, including KMT2D, FAT1, and NOTCH1 were more frequently identified in patients with ESCC from NC. Furthermore, some genes (TDG and DNAH8) correlated with overall survival in completely opposite ways in patients with ESCC from different geographical regions. Conclusions: Our study provides insights into genomic differences in ESCC among different regions. These differences may be related to differences in environmental carcinogens, incidence, and survival.

Accurate diagnosis of lung tissues for 2D Raman spectrogram by deep learning based on short-time Fourier transform.

Multivariate statistical analysis methods have an important role in spectrochemical analyses to rapidly identify and diagnose cancer and the subtype. However, utilizing these methods to analyze lager amount spectral data is challenging, and poses a major bottleneck toward achieving high accuracy. Here, a new convolutional neural networks (CNN) method based on short-time Fourier transform (STFT) to diagnose lung tissues via Raman spectra readily is proposed. The models yield that the accuracies of the new method are higher than the conventional methods (principal components analysis -linear discriminant analysis and support vector machine) for validation group (95.2% vs 85.5%, 94.4%) and test group (96.5% vs 90.4%, 93.9%) after cross-validation. The results illustrate that the new method which converts one-dimensional Raman data into two-dimensional Raman spectrograms improve the discriminatory ability of lung tissues and can achieve automatically accurate diagnosis of lung tissues.

A substantial increase of analytical throughput in capillary electrophoresis throughput by separation-interrupted sequential injections.

How to further improve the throughput of capillary electrophoresis (CE) is a fascinating question. Herein an idea to substantially increase the throughput of CE has been proposed together with theory and experimental demonstration. The key is to introduce samples for CE, one after another, by a short suspension of voltage application, which was hence termed separation-interrupted sequential injections (Sisi). The idea was demonstrated to be feasible on a laboratory-built CE instrument coupled with tandem C4D (contactless capacitively-coupled conductivity) detectors. At least 50 injections of a testing sample (mixture of NH4+, K+, Ca2+, Na+ and Mg2+) were successfully separated in only a single run. The separation took 145 min in total, equivalent to 2.9 min per analysis which is only 21% of that of normal CE. Quantification of the separated ions was performed, with a limit of detection of 1.1-2.6 µM, a limit of quantification of 3.2-8.9 µM, and a linear range up to 1000 µM (R2 > 0.99). The recovery was between 88% and 112% measured by spiking standards into samples at low, middle and high levels. The real applicability of Sisi-CE was evaluated by direct injection and analysis of 45 mineral water samples also in a single run. Its clinical application potential was demonstrated by high throughput assay of the calcium and zinc gluconate oral solution formula, and the blood potassium of hyperkalemia and hypokalemia from patients with renal failure disease. This method can be extended to other applications such as omics studies through the use of more suitable detectors. The theory proposed may also be applicable to other high throughput methods.

Study Design and Data Analysis of Artificial Pancreas Device Systems with Closed-Loop Glucose-Sensing Insulin Delivery.

OBJECTIVE: The objective of this article is to provide a high-profile review and discussion on the study design and statistical analysis of pivotal clinical trials conducted to demonstrate the safety and effectiveness of closed-loop investigational artificial pancreas device systems (APDSs) in premarket approval applications. METHODS: The United States Food and Drug Administration (FDA) guidance on the content of investigational device exemption and premarket approval applications for APDSs is reviewed with special emphasis on study design and statistical analysis of the pivotal clinical trials. The two pivotal studies for the MiniMed 670G hybrid closed-loop system by Medtronic in their premarket approval application are summarized and discussed. RESULTS: The United States FDA established detailed recommendations on the study design and statistical analysis of pivotal clinical trials for the industry that seek market investigational APDSs and for FDA scientific reviewers that regulate the device applications. The recommendations cover specifics regarding patient population, clinical endpoints, and strategies for data analysis. However, the two pivotal studies that demonstrated the effectiveness of the FDA-approved MiniMed 670G hybrid closed-loop system were not typical randomized controlled trials as per FDA recommendations. CONCLUSION: The development and regulation of investigational APDSs require careful and sophisticated clinical study designs and data analysis in premarket approval applications. The regulatory evaluation process of the APDSs is rather complicated since the devices consist of multiple components that collaboratively function to mimic human pancreases.

Association between microRNA-146a, -499a and -196a-2 SNPs and non-small cell lung cancer: a case-control study involving 2249 subjects.

MicroRNA (miR) acts as a negative regulator of gene expression. Many literatures have suggested that miRs may be involved in the process of cell proliferation, inflammation, oxidative stress, energy metabolism and epithelial-mesenchymal transition. Thus, miRs may be implicated in the occurrence of non-small cell lung cancer (NSCLC). In the current investigation, we included 2249 subjects (1193 NSCLC patients and 1056 controls) and designed a study to identify the relationship of miR-146a rs2910164 C/G, -499a rs3746444 A/G and -196a-2 rs11614913 T/C with the risk of NSCLC. The risk factors (e.g., body mass index (BMI), sex, smoking, drinking and age) was used to adjust the odds ratios (ORs) and 95% confidence intervals (CIs). After conducting a power value assessment, we did not confirm that the miR-single nucleotide polymorphisms (SNPs) genotypic distributions were different in NSCLC cases and controls. However, the association of miR-196a-2 rs11614913 with a decreased risk of NSCLC was identified in the female subgroup (adjusted P=0.005, power = 0.809 for TC vs. TT, and adjusted P=0.004, power = 0.849 for CC/TC vs. TT). In addition, gene-gene interaction analysis showed that rs11614913 TC/3746444 AA and rs11614913 CC/rs3746444 AA could also reduce the susceptibility to NSCLC (rs11614913 TC/rs3746444 AA vs. rs11614913 TT/rs3746444 AA, P=0.001, power = 0.912 and rs11614913 CC/rs3746444 AA vs. rs11614913 TT/rs3746444 AA, P=0.003, power = 0.836). In conclusion, in overall comparisons, we did not confirm that the rs2910164, rs3746444, and rs11614913 SNPs genotypic distributions were different in NSCLC cases and controls. However, this case-control study demonstrates that miR-196a-2 rs11614913 may be a protective factor for the development of NSCLC among female patients.