Isolated non-immune mediated second-degree atrioventricular block in fetus: natural history and predictive factors for spontaneous recovery

OBJECTIVES

To uncover the clinical course of fetal isolated non-immune mediated second-degree AVB and determine the factors associated with the spontaneous recovery for fetal non-immune second-degree atrioventricular block (AVB).

METHODS

A total of 20 fetuses with isolated, non-immune mediated second-degree AVB were prospectively recruited between 2014 and 2022. These fetuses were divided into the spontaneous recovery group (n=12) and the non-spontaneous recovery group (n=8). Maternal and fetal basic characteristics, intrauterine and postnatal outcomes were compared between groups.

RESULTS

Twelve fetuses restored 1:1 atrioventricular conduction in utero and did not recur during the postnatal follow-up period. The residual eight fetuses maintained as second-degree AVB and six of them were aborted due to parental request in utero. Of the two live children with second-degree AVB, one of them progressed to complete AVB at the latest follow up at the age of 34 months, but without any symptoms, heart enlargement or dysfunction. The residual one progressed to complete AVB and was finally diagnosed with type 2 long-QT syndrome. Fetuses in the spontaneous recovery group presented with earlier gestational age at diagnosis (20.0[17.0-26.0] vs. 24.5[18.0-35.0] weeks, p=0.004) and higher atrial rate (147[130-160] vs 138.00[125.00-149.00] bpm, p=0.006) in comparison with the non-spontaneous recovery group. A cut-off value of 22.5 weeks of gestational age and 144 bpm of atrial rate at diagnosis could predict the failure of spontaneous recovery, with sensitivities of 87.5%, 75%, and specificities of 92.0%, 87.5%, respectively.

CONCLUSIONS

The outcome of fetal non-immune second-degree AVB was favorable. Earlier gestational age at diagnosis and higher atrial rate were related to spontaneous reversion for isolated non-immune-mediated second-degree AVB. However, prenatal gene test should be performed for those with persistent AVB to exclude the heritable disorders including LQTS. These findings may provide important references for clinical management and prenatal counseling. This article is protected by copyright. All rights reserved.

Harnessing the Genetic Basis of Sorghum Biomass-Related Traits to Facilitate Bioenergy Applications

The extensive use of fossil fuels and global climate change have raised ever-increasing attention to sustainable development, global food security and the replacement of fossil fuels by renewable energy. Several C4 monocot grasses have excellent photosynthetic ability, stress tolerance and may rapidly produce biomass in marginal lands with low agronomic inputs, thus representing an important source of bioenergy. Among these grasses, Sorghum bicolor has been recognized as not only a promising bioenergy crop but also a research model due to its diploidy, simple genome, genetic diversity and clear orthologous relationship with other grass genomes, allowing sorghum research to be easily translated to other grasses. Although sorghum molecular genetic studies have lagged far behind those of major crops (e.g., rice and maize), recent advances have been made in a number of biomass-related traits to dissect the genetic loci and candidate genes, and to discover the functions of key genes. However, molecular and/or targeted breeding toward biomass-related traits in sorghum have not fully benefited from these pieces of genetic knowledge. Thus, to facilitate the breeding and bioenergy applications of sorghum, this perspective summarizes the bioenergy applications of different types of sorghum and outlines the genetic control of the biomass-related traits, ranging from flowering/maturity, plant height, internode morphological traits and metabolic compositions. In particular, we describe the dynamic changes of carbohydrate metabolism in sorghum internodes and highlight the molecular regulators involved in the different stages of internode carbohydrate metabolism, which affects the bioenergy utilization of sorghum biomass. We argue the way forward is to further enhance our understanding of the genetic mechanisms of these biomass-related traits with new technologies, which will lead to future directions toward tailored designing sorghum biomass traits suitable for different bioenergy applications.

Modeling and insights into the structural characteristics of endocrine-disrupting chemicals

Endocrine-disrupting chemicals (EDCs) can cause serious harm to human health and the environment; therefore, it is important to rapidly and correctly identify EDCs. Different computational models have been proposed for the prediction of EDCs over the past few decades, but the reported models are not always easily available, and few studies have investigated the structural characteristics of EDCs. In the present study, we have developed a series of artificial intelligence models targeting EDC receptors: the androgen receptor (AR); estrogen receptor (ER); and pregnane X receptor (PXR). The consensus models achieved good predictive results for validation sets with balanced accuracy values of 87.37%, 90.13%, and 79.21% for AR, ER, and PXR binding assays, respectively. Analysis of the physical-chemical properties suggested that several chemical properties were significantly (p < 0.05) different between EDCs and non-EDCs. We also identified structural alerts that can indicate an EDC, which were integrated into the web server SApredictor. These models and structural characteristics can provide useful tools and information in the discrimination and mechanistic understanding of EDCs in drug discovery and environmental risk assessment.

Association of proton pump inhibitors with gastric and colorectal cancer risk: A systematic review and meta-analysis

Background: Proton pump inhibitors (PPI) are generally considered to be one of the well-established prescription drug classes and are commonly used to treat most acid-related diseases. However, a growing body of literature showing an association between gastric and colorectal cancer risk and PPI use continues to raise concerns about the safety of PPI use. Therefore, we aimed to investigate the association between proton pump inhibitor use and risk of gastric and colorectal cancer.

Methods: We collected relevant articles using PubMed, Embase, Web of Science and Cochrane library from 1 January 1990 to 21 March 2022. The pooled effect sizes were calculated based on the random-effects model. The study was registered with PROSPERO (CRD42022351332).

Results: A total of 24 studies (n = 8,066,349) were included in the final analysis in the screening articles. Compared with non-PPI users, PPI users had a significantly higher risk of gastric cancer (RR = 1.82, 95% CI: 1.46–2.29), but not colorectal cancer (RR = 1.22, 95% CI: 0.95–1.55). Subgroup analysis showed that there was a significant positive correlation between the use of PPI and the risk of non-cardiac cancer (RR = 2.75, 95% CI: 2.09–3.62). There was a significant trend between the duration dependent effect of PPI use and the risk of gastric cancer (&lt;1 year RR = 1.56, 95% CI: 1.30–1.86; 1–3 years RR = 1.75, 95% CI: 1.28–2.37; &gt;3 years RR = 2.32, 95% CI: 1.15–4.66), but not colorectal cancer (≤1 year RR = 1.00, 95% CI: 0.78–1.28; &gt;1 year RR = 1.18, 95% CI: 0.91–1.54; ≥5 years RR = 1.06, 95% CI: 0.95–1.17).

Conclusion: We found that PPI use increased gastric cancer risk, but not colorectal cancer risk. This result may be biased due to confounding factors. More prospective studies are needed to further validate and support our findings.

Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022351332], identifier [CRD42022351332].

Machine Learning Modeling and Insights into the Structural Characteristics of Drug-Induced Neurotoxicity

Neurotoxicity can be resulted from many diverse clinical drugs, which has been a cause of concern to human populations across the world. The detection of drug-induced neurotoxicity (DINeurot) potential with biological experimental methods always required a lot of budget and time. In addition, few studies have addressed the structural characteristics of neurotoxic chemicals. In this study, we focused on the computational modeling for drug-induced neurotoxicity with machine learning methods and the insights into the structural characteristics of neurotoxic chemicals. Based on the clinical drug data with neurotoxicity effects, we developed 35 different classifiers by combining five different machine learning methods and seven fingerprint packages. The best-performing model achieved good results on both 5-fold cross-validation (balanced accuracy of 76.51%, AUC value of 0.83, and MCC value of 0.52) and external validation (balanced accuracy of 83.63%, AUC value of 0.87, and MCC value of 0.67). The model can be freely accessed on the web server DINeuroTpredictor (http://dineurot.sapredictor.cn/). We also analyzed the distribution of several key molecular properties between neurotoxic and non-neurotoxic structures. The results indicated that several physicochemical properties were significantly different between the neurotoxic and non-neurotoxic compounds, including molecular polar surface area (MPSA), AlogP, the number of hydrogen bond acceptors (nHAcc) and donors (nHDon), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). In addition, 18 structural alerts responsible for chemical neurotoxicity were identified. The structural alerts have been integrated with our web server SApredictor (http://www.sapredictor.cn). The results of this study could provide useful information for the understanding of the structural characteristics and computational prediction for chemical neurotoxicity.

Modeling and insights into the structural characteristics of drug-induced autoimmune diseases

The incidence and complexity of drug-induced autoimmune diseases (DIAD) have been on the rise in recent years, which may lead to serious or fatal consequences. Besides, many environmental and industrial chemicals can also cause DIAD. However, there are few effective approaches to estimate the DIAD potential of drugs and other chemicals currently, and the structural characteristics and mechanism of action of DIAD compounds have not been clarified. In this study, we developed the in silico models for chemical DIAD prediction and investigated the structural characteristics of DIAD chemicals based on the reliable drug data on human autoimmune diseases. We collected 148 medications which were reported can cause DIAD clinically and 450 medications that clearly do not cause DIAD. Several different machine learning algorithms and molecular fingerprints were combined to develop the in silico models. The best performed model provided the good overall accuracy on validation set with 76.26%. The model was made freely available on the website http://diad.sapredictor.cn/. To further investigate the differences in structural characteristics between DIAD chemicals and non-DIAD chemicals, several key physicochemical properties were analyzed. The results showed that AlogP, molecular polar surface area (MPSA), and the number of hydrogen bond donors (nHDon) were significantly different between the DIAD and non-DIAD structures. They may be related to the DIAD toxicity of chemicals. In addition, 14 structural alerts (SA) for DIAD toxicity were detected from predefined substructures. The SAs may be helpful to explain the mechanism of action of drug induced autoimmune disease, and can used to identify the chemicals with potential DIAD toxicity. The structural alerts have been integrated in a structural alert-based web server SApredictor (http://www.sapredictor.cn). We hope the results could provide useful information for the recognition of DIAD chemicals and the insights of structural characteristics for chemical DIAD toxicity.

Modeling and insights into the structural basis of chemical acute aquatic toxicity

It has become a top global regulatory priority to prevent and control pollution from the release of synthetic chemicals, which continues to affect the aquatic communities. In the past decades, computational tools were largely used to significantly reduce the budget and time cost of chemical acute aquatic toxicity assessment. But the structural basis of toxic compounds was rarely analyzed. In the present study, we collected 1438, 485 and 961 chemicals with acute toxicity data records for three representative aquatic species, including Tetrahymena pyriformis, Daphnia magna, and Fathead minnow, respectively. A series of artificial intelligence models were developed using OCHEM tools. For each aquatic toxicity endpoint, a consensus model was developed based on the top performed individual models. The consensus models provided good performance on external validation sets with total accuracy values 96.88 %, 90.63 %, and 84.90 % for Tetrahymena pyriformis toxicity (TPT), Daphnia magna toxicity (DMT), and Fathead minnow toxicity (FMT), respectively. The models can be freely accessed via https://ochem.eu/article/146910. Moreover, the analysis of physical-chemical properties suggested that several key molecular properties of aquatic toxic compounds were significantly different with those of non-toxic compounds. Thus, these descriptors may be associated to chemical acute aquatic toxicity, and may be useful for the understand of chemical aquatic toxicity. Besides, in this study, the structural alerts for aquatic toxicity were detected using f-score and frequency ratio analysis of predefined substructures. A total of 112, 58 and 33 structural alerts were identified responsible for TPT, DMT, and FMT, respectively. These structural alerts could provide useful information for the mechanisms of chemical aquatic toxicity and visual alerts for environmental assessment. All the structural alerts were integrated in the web-server SApredictor (www.sapredictor.cn).

SApredictor: An Expert System for Screening Chemicals Against Structural Alerts

The rapid and accurate evaluation of chemical toxicity is of great significance for estimation of chemical safety. In the past decades, a great number of excellent computational models have been developed for chemical toxicity prediction. But most machine learning models tend to be “black box”, which bring about poor interpretability. In the present study, we focused on the identification and collection of structural alerts (SAs) responsible for a series of important toxicity endpoints. Then, we carried out effective storage of these structural alerts and developed a web-server named SApredictor (www.sapredictor.cn) for screening chemicals against structural alerts. People can quickly estimate the toxicity of chemicals with SApredictor, and the specific key substructures which cause the chemical toxicity will be intuitively displayed to provide valuable information for the structural optimization by medicinal chemists.

Highly efficient generation of narrowband terahertz radiation driven by a two-spectral-line laser in PPLN

We demonstrate record ∼0.9% efficiencies for optical conversion to narrowband (<1% relative bandwidth) terahertz (THz) radiation by strongly cascaded difference frequency generation. These results are achieved using a novel, to the best of our knowledge, laser source, customized for high efficiencies, with two narrow spectral lines of variable separation and pulse duration (≥250 ps). THz radiation generation in 5% MgO-doped periodically poled lithium niobate (PPLN) crystals of varying poling period was explored at cryogenic and room temperature operation as well as with different crystal lengths. This work addresses an increasing demand for high-field THz radiation pulses which has, up to now, been largely limited by low optical-to-THz radiation conversion efficiencies.

Stabilization and sensory evaluation of calcium-enriched soymilk prepared using different chelating agents

It is generally acknowledged that calcium lactate (ca-lactate) fortification of soymilk to achieve the equivalent calcium level in cow’s milk (1.2 mg/g) causes instability in soymilk. In order to produce acceptable calcium-enriched soymilk with great stability, three different kinds of chelating agents were added, namely tripotassium citrate (TPC), trisodium citrate (TSC), and sodium hexametaphosphate (SHMP). In this study, the addition of ca-lactate was found to reduce the pH (6.62 to 5.37) and increase the particle diameter (268.66 to 1,222.81 nm) and sedimentation (0.19 to 8.75%) of soymilk. Meanwhile, the addition of TPC and TSC produced calcium-enriched soymilks with smaller particle diameter (295.21-452.22 nm and 297.61-461.80 nm) and lower sedimentation levels (0.25-1.55% and 0.26-1.58%). The pH of calcium-enriched soymilk was also increased when both chelating agents were added. The application of SHMP as a chelating agent was not as effective as TPC and TSC because the particle diameter (527.98-703.40 nm) and sedimentation level were still relatively higher (4.24-5.68%) than the unfortified soymilk. Even though both TPC and TSC showed no significant difference (P>0.05) in stability, the sensory evaluation showed different results. Soymilk with TSC added was saltier and sourer than samples with TPC added. The study recommends the use of TPC as the chelating agent in soymilk enriched with ca-lactate.

In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity

Drug induced nephrotoxicity is a major clinical challenge, and it is always associated with higher costs for the pharmaceutical industry and due to detection during the late stages of drug development. It is desirable for improving the health outcomes for patients to distinguish nephrotoxic structures at an early stage of drug development. In this study, we focused on in silico prediction and insights into the structural basis of drug induced nephrotoxicity, based on reliable data on human nephrotoxicity. We collected 565 diverse chemical structures, including 287 nephrotoxic drugs on humans in the real world, and 278 non-nephrotoxic approved drugs. Several different machine learning and deep learning algorithms were employed for in silico model building. Then, a consensus model was developed based on three best individual models (RFR_QNPR, XGBOOST_QNPR, and CNF). The consensus model performed much better than individual models on internal validation and it achieved prediction accuracy of 86.24% external validation. The results of analysis of molecular properties differences between nephrotoxic and non-nephrotoxic structures indicated that several key molecular properties differ significantly, including molecular weight (MW), molecular polar surface area (MPSA), AlogP, number of hydrogen bond acceptors (nHBA), molecular solubility (LogS), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). These molecular properties may be able to play an important part in the identification of nephrotoxic chemicals. Finally, 87 structural alerts for chemical nephrotoxicity were mined with f-score and positive rate analysis of substructures from Klekota-Roth fingerprint (KRFP). These structural alerts can well identify nephrotoxic drug structures in the data set. The in silico models and the structural alerts could be freely accessed via https://ochem.eu/article/140251 and http://www.sapredictor.cn, respectively. We hope the results should provide useful tools for early nephrotoxicity estimation in drug development.

Average SAR prediction, validation, and evaluation for a compact MR scanner head-sized RF coil

A recently developed compact 3 T (C3T) MRI scanner with high performance gradients [1, 2] has a dedicated radiofrequency (RF) transmit coil that exposes only the head, neck and a small portion of the upper body region during head-first scanning. Due to the unique coil geometry and patient positioning, the established SAR model used for a conventional whole-body scanner cannot be directly translated to the C3T. Here a specific absorption rate (SAR) estimation and validation framework was developed and used to implement a dedicated and accurate SAR prediction model for the C3T. Two different SAR prediction models for the C3T were defined and evaluated: one based on an anatomically derived exposed mass, and one using a fixed anatomical position located caudally to the RF coil to determine the exposed mass. After coil modeling and virtual human body simulation, the designed SAR prediction model was implemented on the C3T and verified with calorimetry and in vivo scan power monitoring. The fixed-demarcation exposed mass model was selected as appropriate exposed mass region to accurately estimate the SAR deposition in the patient on the C3T.

In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods

Chemical-induced hematotoxicity is an important concern in the drug discovery, since it can often be fatal when it happens. It is quite useful for us to give special attention to chemicals which can cause hematotoxicity. In the present study, we focused on in silico prediction of chemical-induced hematotoxicity with machine learning (ML) and deep learning (DL) methods. We collected a large data set contained 632 hematotoxic chemicals and 1525 approved drugs without hematotoxicity. Computational models were built using several different machine learning and deep learning algorithms integrated on the Online Chemical Modeling Environment (OCHEM). Based on the three best individual models, a consensus model was developed. It yielded the prediction accuracy of 0.83 and balanced accuracy of 0.77 on external validation. The consensus model and the best individual model developed with random forest regression and classification algorithm (RFR) and QNPR descriptors were made available at https://ochem.eu/article/135149 , respectively. The relevance of 8 commonly used molecular properties and chemical-induced hematotoxicity was also investigated. Several molecular properties have an obvious differentiating effect on chemical-induced hematotoxicity. Besides, 12 structural alerts responsible for chemical hematotoxicity were identified using frequency analysis of substructures from Klekota-Roth fingerprint. These results should provide meaningful knowledge and useful tools for hematotoxicity evaluation in drug discovery and environmental risk assessment.

In silico prediction of drug-induced ototoxicity using machine learning and deep learning methods

Drug-induced ototoxicity has become a serious global problem, because of leading to deafness in hundreds of thousands of people every year. It always results from exposure to drugs or environmental chemicals that cause the impairment and degeneration of the inner ear. Herein, we focused on the in silico modeling of drug-induced ototoxicity of chemicals. We collected 1,102 ototoxic medications and 1,705 non-ototoxic drugs. Based on the data set, a series of computational models were developed with different traditional machine learning and deep learning algorithms implemented on an online chemical database and modeling environment. Six ML models performed best on 5-fold cross-validation and test set. A consensus model was developed with the best individual models. These models were further validated with an external validation. The consensus model showed best predictive ability, with high accuracy of 0.95 on test set and 0.90 on validation set. The consensus model and the data sets used for model development are available at https://ochem.eu/model/46566321. Besides, 16 structural alerts responsible for drug-induced ototoxicity were identified. We hope the results could provide meaningful knowledge and useful tools for ototoxicity evaluation in drug discovery and environmental risk assessment.

Five lncRNAs associated with the survival of hepatocellular carcinoma: a comprehensive study based on WGCNA and competing endogenous RNA network

OBJECTIVE

The competing endogenous RNA (ceRNA) presents a comprehensive regulatory network among lncRNAs, miRNAs and mRNA. The ceRNA provides significant information in understanding the pathology of cancer. This study aimed to explore a lncRNA-associated ceRNA network for predicting the overall survival of patients with hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

In this study, RNA-sequencing data of HCC were downloaded from The Cancer Genomes Atlas (TCGA) database. The module-trait relationship was analyzed with Weighted gene co-expression network analysis (WGCNA). The key module associated with tumor was identified, as well as the involved lncRNAs, mRNAs and miRNAs. The preliminary ceRNA network was constructed with Cytoscape. The survival analysis was further performed to screen survival-relevant lncRNAs, mRNAs and miRNAs, and then the survival-associated ceRNA network was reconstructed.

RESULTS

Eventually, 5 lncRNAs, 10 miRNAs, and 25 mRNAs were included in the reconstructed ceRNA network.

CONCLUSIONS

The identified lncRNAs were promising candidate biomarkers in HCC diagnosis and therapeutics. This analysis process was effective to construct ceRNA network. The result will be conductive to explore the significant lncRNAs and regulatory mechanism.

Development of a PET/EPRI combined imaging system for assessing tumor hypoxia

Precise quantitative delineation of tumor hypoxia is essential in radiation therapy treatment planning to improve the treatment efficacy by targeting hypoxic sub-volumes. We developed a combined imaging system of positron emission tomography (PET) and electron para-magnetic resonance imaging (EPRI) of molecular oxygen to investigate the accuracy of PET imaging in assessing tumor hypoxia. The PET/EPRI combined imaging system aims to use EPRI to precisely measure the oxygen partial pressure in tissues. This will evaluate the validity of PET hypoxic tumor imaging by (near) simultaneously acquired EPRI as ground truth. The combined imaging system was constructed by integrating a small animal PET scanner (inner ring diameter 62 mm and axial field of view 25.6 mm) and an EPRI subsystem (field strength 25 mT and resonant frequency 700 MHz). The compatibility between the PET and EPRI subsystems were tested with both phantom and animal imaging. Hypoxic imaging on a tumor mouse model using 18F-fluoromisonidazole radio-tracer was conducted with the developed PET/EPRI system. We report the development and initial imaging results obtained from the PET/EPRI combined imaging system.

MicroRNA-1286 inhibits osteogenic differentiation of mesenchymal stem cells to promote the progression of osteoporosis via regulating FZD4 expression

OBJECTIVE

The aim of this study was to investigate whether microRNA-1286 could inhibit the osteogenic differentiation of human marrow mesenchymal stem cells (hMSCs) by regulating FZD4 expression and promoting the progression of osteoporosis.

PATIENTS AND METHODS

Quantitative Real Time-Polymerase Chain Reaction (qRT-PCR) was used to detect the expression of microRNA-1286 in the serum of patients with osteoporosis. Meanwhile, microRNA-1286 expression in different stages of osteogenic differentiation of hMSCs was measured by qRT-PCR as well. After overexpression of microRNA-1286 and FZD4 in hMSCs, the mRNA expression levels of microRNA-1286, alkaline phosphatase (ALP), RUNX2 and osteocalcin (OCN) were detected by qRT-PCR. The protein expression levels of RUNX2 and OCN were detected by Western blot. Meanwhile, alkaline phosphatase (ALP) activity and expression in cells were examined using ALP assay kit and ALP staining method, respectively. Cell mineralized nodules were detected through the alizarin red staining test. Bioinformatics method was used to predict the binding site of microRNA-1286 to FZD4. Subsequent luciferase reporter gene assay was performed to verify whether microRNA-1286 could combine with FZD4. After overexpression or knockdown of microRNA-1286, the mRNA and protein expressions of FZD4 were analyzed using qRT-PCR and Western blot assay, respectively. After the simultaneous overexpression of microRNA-1286 and FZD4 in hMSCs, the mRNA expression levels of ALP, RUNX2 and OCN, ALP activity and content, and cell mineralization ability were successively examined.

RESULTS

The expression of microRNA-1286 in the serum of patients with osteoporosis was significantly higher than that of the normal population. Meanwhile, microRNA-1286 expression decreased with the increase of osteogenic differentiation days of hAMSCs. After the overexpression of microRNA-1286, ALP, RUNX2, and OCN levels, ALP activity, RUNX2, and OCN protein levels, as well as mineralized nodule formation were significantly reduced. However, results were reversed when FZD4 was simultaneously up-regulated. Luciferase reporter gene assay results verified that microRNA-1286 could bind to FZD4. After the overexpression of microRNA-1286, the mRNA and protein expressions of FZD4 were found significantly down-regulated. However, results were reversed after knocking down microRNA-1286. Furthermore, the simultaneous overexpression of microRNA-1286 and FZD4 could counteract the inhibitory effect of over-expression of microRNA-1286 on osteogenic differentiation of hMSCs.

CONCLUSIONS

MicroRNA-1286 can regulate FZD4 expression and inhibit osteogenic differentiation of hMSCs, thereby promoting the development of osteoporosis.

Design, evaluation and initial imaging results of a PET insert based on strip-line readout for simultaneous PET/MRI

We present the development of a PET insert system for potential simultaneous PET/MR imaging using a 9.4 T small animal MRI scanner to test our system. The detectors of the system adopt a strip-line based multiplexing readout method for SiPM signals. In this readout, multiple SiPM outputs in a row share a common strip-line. The position information about a hit SiPM is encoded in the propagation time difference of the signals arriving at the two ends of the strip-line. The use of strip-lines allows us to place the data acquisition electronics remotely from the detector module to greatly simplify the design of the detector module and minimize the mutual electromagnetic interference. The prototype is comprised of 14 detector modules, each of which consists of an 8x4 LYSO scintillator array (each LYSO crystal is 3x3x10 mm³) coupled to two units of Hamamatsu MPPC arrays (4x4, 3.2 mm pitch) that are mounted on a strip-line board. On the strip-line board, outputs of the 32 SiPMs are routed to 2 strip-lines so that 16 SiPM signals share a strip-line. The detector modules are installed inside a plastic cylindrical supporting structure with an inner and outer diameter of 60 mm and 115 mm, respectively, to fit inside a Bruker BioSpec 9.4 Tesla MR scanner. The axial field of view of the prototype is 25.4 mm. The strip-lines were extended by using 5-meter cables to a sampling data acquisition (DAQ) board placed outside the magnet. The detectors were not shielded in the interest of investigating how they may affect and be affected by the MRI. Experimental tests were conducted to evaluate detection performance, and phantom and animal imaging were carried out to assess the spatial resolution and the MR compatibility of the PET insert. Initial results are encouraging and demonstrate that the prototype insert PET can potentially be used for PET/MR imaging if appropriate shielding will be implemented for minimizing the mutual interference between the PET and MRI systems.

Diversity and abundance of comammox bacteria in the sediments of an urban lake

AIMS

Although comammox have been discovered in a variety of ecosystems, there are few studies in urban lakes. This paper attempted to confirm whether this ammonia-oxidizing microbe exists in urban lakes and to determine the factors influencing its existence.

METHODS AND RESULTS

This study investigated the diversity and abundance of comammox bacteria in sediments of a typical urban lake in China, and their ecological relationship with other ammonia-oxidizing micro-organisms. The phylogenetic analysis indicated that comammox clade A existed in the sediment of Lake Donghu, and the comammox bacteria co-existed with ammonia-oxidizing archaea (AOA), ammonia-oxidizing bacteria (AOB) and anaerobic ammonia-oxidizing (anammox) bacteria in the sediment of this lake. The abundances of the ammonia monooxygenase subunit A (amoA) genes for comammox, AOA, AOB and anammox 16S rRNA were 2·43 × 10⁸ , 1·07 × 10⁸ , 3·24 × 10⁷ and 3·21 × 10¹¹ copies per gram dry sediment respectively. Moreover, the amoA gene abundance of comammox was positively correlated with that of AOA and AOB. The redundancy analysis showed that the abundance of the comammox amoA gene was negatively correlated with the concentration of main indicators for nitrogen status in both the sediment and the water column, indicating that eutrophication may inhibit the growth of comammox bacteria.

CONCLUSIONS

Comammox bacteria play an important ecological role in the nitrogen cycle of urban lake sediments.

SIGNIFICANCE AND IMPACT OF THE STUDY

Our results indicated comammox bacteria were widespread in urban lakes and eutrophication may inhibit their growth.

Frequency-comb-based laser system producing stable optical beat pulses with picosecond durations suitable for high-precision multi-cycle terahertz-wave generation and rapid detection

We generate temporally modulated optical pulses with a beat frequency of 255 GHz, a duration of 360 ps, and a repetition rate of 2 MHz. The temporal envelope, beat frequency, and repetition rate are computer-programmable. A frequency comb serves as a phase and frequency reference for the locking of two laser lines. The system enables beat frequencies that are adjustable in steps of the frequency comb's repetition rate and exhibit Hz-level precision and accuracy. We expect the optical beat pulses to be well suited for versatile multi-cycle terahertz-wave generation with controllable carrier-envelope phase. We demonstrate that the inherent synchronization of the frequency comb's ultra-short pulse train and the synthesized optical beat (or later the multi-cycle terahertz) pulses enables rapid and phase-sensitive sampling of such pulses.

[Clinical characteristics analysis of children with reversible posterior leukoen-cephalopathy syndrome during the treatment of hematological tumor]

OBJECTIVE

To analyze the clinical characteristics of patients with hematological tumor or disease before and after reversible posterior leukoen-cephalopathy syndrome (RPLS).

METHODS

Five patients were both from Peking University First Hospital Pediatric Hematology-oncology Department in the period from March 2012 to March 2017. The gender, age, BMI, underlying diseases, with or without renal damage, hypertension family history, clinical manifestations of convulsions, hemoglobin, and blood pressure, serum sodium levels before and after convulsion, and other data of the children with RPLS were retrospectively analyzed. In the meantime, we followed up the five patients for 6 months to 66 months, kept a watchful eye on their original condition and the recovery of symptoms and signs of the nervous system. The relevant literature was reviewed.

RESULTS

All of the subjects were females in school-age or pre-school age. The underlying diseases were malignant tumor associated with renal involvement or on one side of nephrectomy in 4 of these subjects, while the other one was refractory autoimmune hemolytic anemia. All of the subjects suffered from mild or moderate anemia. The day before RPLS occurred they received chemotherapy made up with cyclophosphamide, vincristine, and actinomycin-D, or the therapy with cyclosporin A and glucocorticoid. The clinical manifestations were afebrile convulsion after getting up in the mooring or in the afternoon. We observed elevation of blood pressure and cutting down of serum sodium compared with themselves. All of the cases recovered soon after management with diazepam, furosemide and amlodipine besylate. Four of them had a good outcomes and did not remain any sequela, while only one girl became childish in emotion and behavior, and then returned gradually to normal two years later. However, by long-term follow-up, the elevation of blood pressure was mainly reviewed in literature.

CONCLUSION

The patients attacked by RPLS, with hematology or oncology cases, could have the underlying disease of renal damage and anemia. Blood pressure elevation and serum sodium falling down at the same time may play an important role during the occurrence of RPLS. Remaining stable of blood pressure and electrolyte level together will possibly reduce or mitigate RPLS.

The expression significance of serum MiR-21 in patients with osteosarcoma and its relationship with chemosensitivity

OBJECTIVE

To investigate the expression level of serum microRNA-21 (miR-21) in 69 patients with osteosarcoma, to analyze the changes in miR-21 expression in the serum and tumor tissues before and after chemotherapy in patients with osteosarcoma, and to investigate the relationship between the expression of serum miR-21 in patients with osteosarcoma and chemosensitivity.

PATIENTS AND METHODS

Real-time polymerase chain reaction (RT-PCR) was used to detect the expression level of serum miR-21 in 69 patients with osteosarcoma before and after chemotherapy, and analyze the relationship between the expression of miR-21 with tumor necrosis grading and chemosensitivity. The expression level of programmed cell death protein 4 (PDCD4) in tumor tissues of patients with osteosarcoma before and after chemotherapy was detected by Western blot.

RESULTS

The expression level of serum miR-21 in patients with osteosarcoma was significantly higher than that in normal control subjects. The expression of serum miR-21 before and after chemotherapy was positively correlated with the expression of miR-21 in the corresponding tissues. In the group where chemotherapy was effective (the effective group), expression levels of miR-21 in patients with osteosarcoma before and after chemotherapy were significantly different. After chemotherapy, the expression level of target genes of miR-21 was increased, and the follow-up results showed that the 5-year survival rate was relatively higher after surgery.

CONCLUSIONS

The expression level of serum miR-21 in patients with osteosarcoma is closely related to the therapeutic effects of osteosarcoma, which can be used as one of the potential biomarkers and therapeutic targets for the diagnosis and prediction of osteosarcoma.

[Autoimmune disorder secondary to DiGeorge syndrome: a long-term follow-up case report and literature review]

DiGeorge syndrome is the most common chromosome microdeletion disease. The classical complications include congenital heart disease, hypothyroidism, immunodeficiency, facial abnormalities, and hypocalcemia. According to whether there is an absence or hypoplasia of the thymus, DiGeorge syndrome can be divided into two types, complete DiGeorge syndrome and partial DiGeorge syndrome. The patient was a female born with congenital heart disease, facial abnormalities and cleft palate. When the patient went to school, she had learning difficulty and had problems in communication and personal social behavior. Breath-holding occurred when she was 6 years old. She got infections about 2-3 times a year, which was easy to be cured each time. Chromosome microdeletion test of peripheral blood showed the classical 22q11.2 microdeletion, and no evidence showed that she has thymus absence, thus her disease was diagnosed as partial DiGeorge syndrome. When the patient was 6 years old, the blood routine test showed slight thrombocytopenia, and reexaminations after that indicated the similar result. When 9 years old, she was found with anemia and severe thrombocytopenia. At the age of 10, the patient was admitted to our hospital, complaining of petechia in the body and mucous of mouth. According to the various examinations results, doctors eventually considered the situation as an autoimmune disorder phenomenon. After being treated by pulse-dose methylprednisolone for three days, the bleeding ceased. Then the patient orally took prednisone acetate and pulse-dose cyclophosphamide, however the thrombocyte and hemoglobin levels had not been back to a normal range. But when the dose of prednisone acetate was reduced, the blood platelet count declined again while the hemoglobin kept normal. The long-term follow-up of this case lasted for more than 20 years. Until now, the patient is taking orally prednisone acetate as a maintainance treatment, and the anemia has been improved since, but thrombocytopenia still exists. The mechanism of DiGeorge syndrome in combination with immunodeficiency is still unclear. The most likely reason is that this phenomenon has some relationship with the dysfunction of the thymus and finally had an effect on the function of T cells. The clinical manifestation is always stubborn and need treatment and follow-up visit for a long time.