Effects of degreasing pretreatment on immunohistochemistry and molecular analysis of gastrointestinal and breast cancer samples.

Lymph node status is a key factor in determining stage, treatment, and prognosis in cancers. Small lymph nodes in the fat-rich gastrointestinal and breast cancer specimens are easily missed in conventional sampling methods. This study examined the effectiveness of degreasing pretreatment with dimethyl sulfoxide (DMSO) in lymph node detection and its impact on the analysis of clinical treatment-related proteins and molecules. Thirty-three cases of gastrointestinal cancer specimens from radical gastrectomy and 63 cases of breast cancer specimens from modified radical mastectomy were included. After routine sampling of lymph nodes, the specimens were immersed in DMSO for 30 minutes for defatting. We assessed changes in the number of detected lymph nodes and pN staging in 33 gastrointestinal cancer specimens and 37 breast cancer specimens. Additionally, we analyzed histological characteristics, Masson's trichrome special staining, and immunohistochemistry (gastrointestinal cancer: MMR, HER2, PD-L1; breast cancer: ER, PR, AR, HER2, Ki-67, PD-L1). Molecular status was evaluated for colorectal cancer (KRAS, NRAS, BRAF, MSI) and breast cancer (HER2) in gastrointestinal cancer specimens and the remaining 26 breast cancer specimens. Compared to conventional sampling, DMSO pretreatment increased the detection rate of small lymph nodes (gastrointestinal cancer: p<0.001; breast cancer: p<0.001) and improved pN staging in one case each of gastric cancer, colon cancer, and rectal cancer (3/33, 9.1%). No significant difference in the morphology, special staining, protein, and molecular status of cancer tissue after DMSO treatment were found. Based on these results and our institutional experience, we recommend incorporating DMSO degreasing pretreatment into clinical pathological sampling practices.

Population genetics of Todarodes pacificus (Cephalopoda: Ommastrephidae) in the northwest Pacific Ocean via GBS sequencing.

The common squid, Todarodes pacificus, is an important commercial species that inhabits the northwest Pacific Ocean, particularly the East Japan Sea, the Pacific coast of Japan, and the East China Sea. In this study, we chose 29 individuals from three areas: one type from the Sea of Japan and two types from the East China Sea. A total of 43,529 SNPs were obtained using genotyping-by-sequencing (GBS). Our analyses revealed low genetic diversity and genetic differentiation in each type. Heterozygote deficiency and inbreeding have caused this low level of genetic diversity. Population structure analysis indicated that the three types were genetically similar, which may be attributed to strong gene flow combined with the demographic history events during the last 2 million years. This new GBS application technique provides valuable information for the conservation of marine species genetics and could be useful for the effective management of this resource.

Genetic polymorphisms and their correlation with dyslipidemia in Chinese patients diagnosed with diabetes mellitus.

BACKGROUND: Dyslipidemia is a common complication in patients with diabetes mellitus (DM) that increases the risk of cardiovascular disease. Genetic polymorphisms have been implicated in the development of dyslipidemia. AIM: To investigate the association between polymorphisms of candidate genes involved in lipid metabolism and dyslipidemia in Chinese patients with DM. METHODS: A cross-sectional study was conducted on 1098 Chinese patients with DM recruited from multiple healthcare centers. Demographic and clinical data were collected, and dyslipidemia was defined according to the National Cholesterol Education Program Adult Treatment Panel III guidelines. Genomic DNA was extracted from blood samples and genotyping for selected polymorphisms of candidate genes (APOE, LPL, CETP, and others) was performed using PCR and DNA sequencing techniques. Statistical analyses were performed using logistic regression models adjusted for potential confounding factors. RESULTS: The study population consisted of 578 males (52.6%) and 520 females (47.4%), with a mean age of 58.4 ± 12.2 years. The prevalence of dyslipidemia was 64.8%. Significant associations were found between dyslipidemia and the APOE rs7412 T/T, APOE rs429358 C/C, LPL rs328 G/G, and CETP rs708272 G/G genotypes after adjusting for covariates. Subgroup analyses showed generally consistent associations across subgroups, although some variations in effect sizes were observed. CONCLUSION: This study identified significant associations between genetic polymorphisms of APOE, LPL, and CETP genes and dyslipidemia in Chinese patients with DM.

Developing a machine learning model for predicting venlafaxine active moiety concentration: a retrospective study using real-world evidence.

BACKGROUND: Venlafaxine is frequently prescribed for patients with depression. To control the concentration of venlafaxine within the therapeutic window for the best treatment effect, a model to predict venlafaxine concentration is necessary. AIM: Our objective was to develop a prediction model for venlafaxine concentration using real-world evidence based on machine learning and deep learning techniques. METHOD: Patients who underwent venlafaxine treatment between November 2019 and August 2022 were included in the study. Important variables affecting venlafaxine concentration were identified using a combination of univariate analysis, sequential forward selection, and machine learning techniques. Predictive performance of nine machine learning and deep learning algorithms were assessed, and the one with the optimal performance was selected for modeling. The final model was interpreted using SHapley Additive exPlanations. RESULTS: A total of 330 eligible patients were included. Five influential variables that affect venlafaxine concentration were venlafaxine daily dose, sex, age, hyperlipidemia, and adenosine deaminase. The venlafaxine concentration prediction model was developed using the eXtreme Gradient Boosting algorithm (R2 = 0.65, mean absolute error = 77.92, root mean square error = 93.58). In the testing cohort, the accuracy of the predicted concentration within ± 30% of the actual concentration was 73.49%. In the subgroup analysis, the prediction accuracy was 69.39% within the recommended therapeutic range of venlafaxine concentration within ± 30% of the actual value. CONCLUSION: The XGBoost model for predicting blood concentration of venlafaxine using real-world evidence was developed, guiding the adjustment of regimen in clinical practice.

Personalized venlafaxine dose prediction using artificial intelligence technology: a retrospective analysis based on real-world data.

BACKGROUND: Venlafaxine dose regimens vary considerably between individuals, requiring personalized dosing. AIM: This study aimed to identify dose-related influencing factors of venlafaxine through real-world data analysis and to construct a personalized dose model using advanced artificial intelligence techniques. METHOD: We conducted a retrospective study on patients with depression treated with venlafaxine. Significant variables were selected through a univariate analysis. Subsequently, the predictive performance of seven models (XGBoost, LightGBM, CatBoost, GBDT, ANN, TabNet, and DT) was compared. The algorithm that demonstrated optimal performance was chosen to establish the dose prediction model. Model validation used confusion matrices and ROC analysis. Additionally, a dose subgroup analysis was conducted. RESULTS: A total of 298 patients were included. TabNet was selected to establish the venlafaxine dose prediction model, which exhibited the highest performance with an accuracy of 0.80. The analysis identified seven crucial variables correlated with venlafaxine daily dose, including blood venlafaxine concentration, total protein, lymphocytes, age, globulin, cholinesterase, and blood platelet count. The area under the curve (AUC) for predicting venlafaxine doses of 75 mg, 150 mg, and 225 mg were 0.90, 0.85, and 0.90, respectively. CONCLUSION: We successfully developed a TabNet model to predict venlafaxine doses using real-world data. This model demonstrated substantial predictive accuracy, offering a personalized dosing regimen for venlafaxine. These findings provide valuable guidance for the clinical use of the drug.

Nitrification inhibitor chlorate and nitrogen substrates differentially affect comammox Nitrospira in a grassland soil.

Introduction: Through the combined use of two nitrification inhibitors, Dicyandiamide (DCD) and chlorate with nitrogen amendment, this study aimed to investigate the contribution of comammox Nitrospira clade B, ammonia oxidizing bacteria (AOB) and archaea (AOA) to nitrification in a high fertility grassland soil, in a 90-day incubation study. Methods: The soil was treated with nitrogen (N) at three levels: 0 mg-N kg-1 soil, 50 mg-N kg-1 soil, and 700 mg-N kg-1 soil, with or without the two nitrification inhibitors. The abundance of comammox Nitrospira, AOA, AOB, and nitrite oxidising bacteria (NOB) was measured using qPCR. The comammox Nitrospira community structure was assessed using Illumina sequencing. Results and Discussion: The results showed that the application of chlorate inhibited the oxidation of both NH4+ and NO2- in all three nitrogen treatments. The application of chlorate significantly reduced the abundance of comammox Nitrospira amoA and nxrB genes across the 90-day experimental period. Chlorate also had a significant effect on the beta diversity (Bray-Curtis dissimilarity) of the comammox Nitrospira clade B community. Whilst AOB grew in response to the N substrate additions and were inhibited by both inhibitors, AOA showed litle or no response to either the N substrate or inhibitor treatments. In contrast, comammox Nitrospira clade B were inhibited by the high ammonium concentrations released from the urine substrates. These results demonstrate the differential and niche responses of the three ammonia oxidising communities to N substrate additions and nitrification inhibitor treatments. Further research is needed to investigate the specificity of the two inhibitors on the different ammonia oxidising communities.

Effects of the ZrO2 Crystalline Phase and Morphology on the Thermocatalytic Decomposition of Dimethyl Methylphosphonate.

Thermocatalytic decomposition is an efficient purification technology that is potentially applicable to degrading chemical warfare agents and industrial toxic gases. In particular, ZrO2 has attracted attention as a catalyst for the thermocatalytic decomposition of dimethyl methylphosphonate (DMMP), which is a simulant of the nerve gas sarin. However, the influence of the crystal phase and morphology on the catalytic performance of ZrO2 requires further exploration. In this study, monoclinic- and tetragonal-phase ZrO2 (m- and t-ZrO2, respectively) with nanoparticle, flower-like shape and hollow microsphere morphologies were prepared via hydrothermal and solvothermal methods, and their thermocatalytic decomposition of DMMP was systematically investigated. For a given morphology, m-ZrO2 performed better than t-ZrO2. For a given crystalline phase, the morphology of hollow microspheres resulted in the longest protection time. The exhaust gases generated by the thermocatalytic decomposition of DMMP mainly comprised H2, CO2, H2O and CH3OH, and the by-products were phosphorus oxide species. Thus, the deactivation of ZrO2 was attributed to the deposition of these phosphorous oxide species on the catalyst surface. These results are expected to help guide the development of catalysts for the safe disposal of chemical warfare agents.

Integrative analysis with machine learning identifies diagnostic and prognostic signatures in neuroblastoma based on differentially DNA methylated enhancers between INSS stage 4 and 4S neuroblastoma.

INTRODUCTION: Accumulating evidence demonstrates that aberrant methylation of enhancers is crucial in gene expression profiles across several cancers. However, the latent effect of differently expressed enhancers between INSS stage 4S and 4 neuroblastoma (NB) remains elusive. METHODS: We utilized the transcriptome and methylation data of stage 4S and 4 NB patients to perform Enhancer Linking by Methylation/Expression Relationships (ELMER) analysis, discovering a differently expressed motif within 67 enhancers between stage 4S and 4 NB. Harnessing the 67 motif genes, we established the INSS stage related signature (ISRS) by amalgamating 12 and 10 distinct machine learning (ML) algorithms across 113 and 101 ML combinations to precisely diagnose stage 4 NB among all NB patients and to predict the prognosis of NB patients. Based on risk scores calculated by prognostic ISRS, patients were categorized into high and low-risk groups according to median risk score. We conducted comprehensive comparisons between two risk groups, in terms of clinical applications, immune microenvironment, somatic mutations, immunotherapy, chemotherapy and single-cell analysis. Ultimately, we empirically validated the differential expressions of two ISRS model genes, CAMTA2 and FOXD1, through immunochemistry staining. RESULTS: Through leave-one-out cross-validation, in both feature selection and model construction, we selected the random forest algorithm to diagnose stage 4 NB, and Enet algorithm to develop prognostic ISRS, due to their highest average C-index across five NB cohorts. After validations, the ISRS demonstrated a stable predictive capability, outperforming the previously published NB signatures and several clinic variables. We stratified NB patients into high and low-risk group based on median risk score, which showed the low-risk group with a superior survival outcome, an abundant immune infiltration, a decreased mutation landscape, and an enhanced sensitivity to immunotherapy. Single-cell analysis between two risk groups reveals biologically cellular variations underlying ISRS. Finally, we verified the significantly higher protein levels of CAMTA2 and FOXD1 in stage 4S NB, as well as their protective prognosis value in NB. CONCLUSION: Based on multi-omics data and ML algorithms, we successfully developed the ISRS to enable accurate diagnosis and prognostic stratification in NB, which shed light on molecular mechanisms of spontaneous regression and clinical utilization of ISRS.

Long-term recurrence of ischemic events in patients with intracranial atherosclerotic stenosis stratified by symptoms and pathogenesis.

BACKGROUND: Intracranial atherosclerotic stenosis (ICAS) can cause either transient ischemic attack (TIA) or acute ischemic stroke (AIS). Pathogenesis of ICAS-AIS can be divided into artery-to-artery embolism(A-A), hypoperfusion(HP), and parent-artery atherosclerosis occluding penetrating artery(POPA). However, the prognosis of each type remains uncertain. Our study aimed to investigate potential disparities in the recurrent risk among these four subtypes of symptomatic ICAS. METHODS: From a prospective, single-center cohort study of acute cerebrovascular diseases from January 2017 to November 2021, we recruited 120 ICAS patients and classified them into four groups based on diffusion weighted imaging. Patients were retrospectively followed up for recurrence in December 2022. The primary outcome was recurrent cerebral vascular events (RCVE) in the same territory. RESULTS: Among 120 recruited patients, POPA(33%) was the most common subtype, followed by A-A(32%), HP(29%), and TIA(6%). Cumulative recurrent rate was 31.2% with median months of follow-up as 27(20-45.5). There was no significant difference in the risk of RCVE in the same territory among four subgroups within three months. However, when considering the risk after three months, TIA(57%) had the highest risk of RCVE, followed by A-A(26%), while HP(4%) and POPA(8%) had lower risks (P = 0.001). Cox regression model indicated that symptom and pathogenesis was an independent risk factor for RCVE in long-term prognosis (P = 0.022), after adjusting for a history of hypertension and cerebral infarction. CONCLUSIONS: Distinctive symptoms and pathogenesis of ICAS exhibit varying risks of RCVE in long-term prognosis. The differentiation in recurrent risk may provide valuable insights for guiding secondary prevention strategies.

Al Doped into Si/P Sites of Na3Zr2Si2PO12 with Conducted Na3PO4 Impurities for Enhanced Ionic Conductivity.

Natrium superionic conductor (NASICON) is a promising solid-state electrolyte because of its high stability under air as well as its safety. Doping is an effective way to improve its ionic conductivity, but there is limited information about the explanation of the doping sites. In this work, Al-doped NASICONs are designed. When Al doping is 0.3 (NAl0.3ZSP), the ionic conductivity is the highest and is 5.08 × 10-5 S cm-1 at 30 °C, which is 3.3 times that of undoped NASICON. NAl0.3ZSP consists of a NASICON structure (monoclinic and rhombohedral phases), an amorphous glassy phase, and Na3PO4 impurities. After Al doping, more Si/P sites are occupied by Al; thus, the ratio of Na3PO4 impurities increases. Na3PO4 at the grain boundary is beneficial for grain boundary resistance decrease, contributing to the decrease of the total resistance. Our work first provides a detailed explanation of doped-Al sites and interprets their effects on ionic conductivity.

Human Mesenchymal Stem Cells-Derived Exosome Mimetic Vesicles Regulation of the MAPK Pathway and ROS Levels Inhibits Glucocorticoid-Induced Apoptosis in Osteoblasts.

Background: Long-term extensive use of glucocorticoids will lead to hormonal necrosis of the femoral head, and osteoblasts play an important role in the prevention of osteonecrosis. However, there is no complete cure for necrosis of the femoral head. Mesenchymal stem cell- (MSCs-) derived exosomes are widely used for the repair of various tissue lesions. Therefore, the aim of this study was to investigate the mechanism of dexamethasone- (DEX-) induced osteoblast apoptosis and the therapeutic effect of human umbilical cord MSC- (hucMSC-) derived exosome mimetic vesicles (EMVs) on osteoblast-induced apoptosis by DEX. Methods: The viability and apoptosis of primary MC3T3-E1 cells were determined by the Cell Counting Kit-8 (CCK-8), FITC-Annexin V/PI staining and immunoblot. The intracellular levels of reactive oxygen species (ROS) after DEX treatment were measured by 2', 7' -dichlorodihydrofluorescein diacetate (DCFH-DA) staining. In this study, hucMSC-EMVs and N-acetyl-l-cysteine (NAC) were used as therapeutic measures. The expression of B-cell lymphoma 2-associated X, Bcl 2, HO-1, and nuclear factor erythroid-derived 2-like 2 and MAPK- signaling pathway in osteogenic cell MC3T3-E1 cells treated with Dex was analyzed by the immunoblotting. Results: DEX significantly induced osteoblasts MC3T3-E1 apoptosis and ROS accumulation. MAPK-signaling pathway was activated in MC3T3-E1 after DEX treatment. hucMSC-EMVs intervention significantly downregulated DEX-induced MAPK-signaling pathway activation and ROS accumulation. In addition, hucMSC-EMVs can reduce the apoptosis levels in osteoblast MC3T3-E1 cells induced by DEX. Conclusions: Our study confirmed that hucMSC-EMVs regulates MAPK-signaling pathway and ROS levels to inhibit DEX-induced osteoblast apoptosis.

Stress response and tolerance mechanisms of spirobudiclofen exposure based on multiomics in Panonychus citri (Acari: Tetranychidae).

The toxicity of insecticides used in the field decreases gradually to sublethal concentrations over time. Therefore, it is necessary to study sublethal effects of pesticides for controlling population explosion. Panonychus citri is a global pest which control is based on insecticides. This study explores the stress responses of spirobudiclofen on the P. citri. Spirobudiclofen significantly inhibited survival and reproduction of P. citri, and the effects aggravated as concentration increased. The transcriptomes and metabolomes of spirobudiclofen-treated and control were compared to characterize spirobudiclofen molecular mechanism. Transcriptomics indicated stress induced by spirobudiclofen stimulated immune defense, antioxidative system, cuticle formation, and lipid metabolism, as deduced from RNA-seq analysis. Meanwhile, our study found that tolerance metabolism in P. citri was regulated by promoting the metabolism of glycerophospholipids, glycine, serine, and threonine. The results of this study can provide a basis for exploring the adaptation strategies of P. citri to spirobudiclofen stress.

Individualized network analysis reveals a link between the gut microbiome, diet intervention and Gestational Diabetes Mellitus.

Gestational Diabetes Mellitus (GDM), a serious complication during pregnancy which is defined by abnormal glucose regulation, is commonly treated by diabetic diet and lifestyle changes. While recent findings place the microbiome as a natural mediator between diet interventions and diverse disease states, its role in GDM is still unknown. Here, based on observation data from healthy pregnant control group and GDM patients, we developed a new network approach using patterns of co-abundance of microorganism to construct microbial networks that represent human-specific information about gut microbiota in different groups. By calculating network similarity in different groups, we analyze the gut microbiome from 27 GDM subjects collected before and after two weeks of diet therapy compared with 30 control subjects to identify the health condition of microbial community balance in GDM subjects. Although the microbial communities remain similar after the diet phase, we find that the structure of their inter-species co-abundance network is significantly altered, which is reflected in that the ecological balance of GDM patients was not "healthier" after the diet intervention. In addition, we devised a method for individualized network analysis of the microbiome, thereby a pattern is found that GDM individuals whose microbial networks are with large deviations from the GDM group are usually accompanied by their abnormal glucose regulation. This approach may help the development of individualized diagnosis strategies and microbiome-based therapies in the future.

The Enhancement of CO Oxidation Performance and Stability in SO2 and H2S Environment on Pd-Au/FeOX/Al2O3 Catalysts.

Carbon monoxide (CO) is a colourless, odourless, and toxic gas. Long-term exposure to high concentrations of CO causes poisoning and even death; therefore, CO removal is particularly important. Current research has focused on the efficient and rapid removal of CO via low-temperature (ambient) catalytic oxidation. Gold nanoparticles are widely used catalysts for the high-efficiency removal of high concentrations of CO at ambient temperature. However, easy poisoning and inactivation due to the presence of SO2 and H2S affect its activity and practical application. In this study, a bimetallic catalyst, Pd-Au/FeOx/Al2O3, with a Au:Pd ratio of 2:1 (wt%) was formed by adding Pd nanoparticles to a highly active Au/FeOx/Al2O3 catalyst. Its analysis and characterisation proved that it has improved catalytic activity for CO oxidation and excellent stability. A total conversion of 2500 ppm of CO at -30 °C was achieved. Furthermore, at ambient temperature and a volume space velocity of 13,000 h-1, 20,000 ppm CO was fully converted and maintained for 132 min. Density functional theory (DFT) calculations and in situ FTIR analysis revealed that Pd-Au/FeOx/Al2O3 exhibited stronger resistance to SO2 and H2S adsorption than the Au/FeOx/Al2O3 catalyst. This study provides a reference for the practical application of a CO catalyst with high performance and high environmental stability.

Plasma metabolomic characterization of SARS-CoV-2 Omicron infection.

Omicron variants of SARS-CoV-2 have spread rapidly worldwide; however, most infected patients have mild or no symptoms. This study aimed to understand the host response to Omicron infections by performing metabolomic profiling of plasma. We observed that Omicron infections triggered an inflammatory response and innate immune, and adaptive immunity was suppressed, including reduced T-cell response and immunoglobulin antibody production. Similar to the original SARS-CoV-2 strain circulating in 2019, the host developed an anti-inflammatory response and accelerated energy metabolism in response to Omicron infection. However, differential regulation of macrophage polarization and reduced neutrophil function has been observed in Omicron infections. Interferon-induced antiviral immunity was not as strong in Omicron infections as in the original SARS-CoV-2 infections. The host response to Omicron infections increased antioxidant capacity and liver detoxification more than in the original strain. Hence, these findings suggest that Omicron infections cause weaker inflammatory alterations and immune responses than the original SARS-CoV-2 strain.

Symptom relationships between internet addiction and anxiety across primary and middle school students during the Omicron lockdown.

During the Omicron pandemic, students in Shenzhen took classes at home via the internet, which could lead to internet addiction (IA) symptoms, and anxiety is often considered an important risk factor for IA. There are several different developmental stages within adolescence. However, no studies have explored the interaction between IA and anxiety at the symptom level using a longitudinal design stratified by age. A total of 2744 students completed the questionnaire 50 days after starting the online classes (T1) and 50 days after they returned to school (T2). A cross-lagged panel network model was used to describe the structure of the comorbidity network. With the help of bootstrapping, the Mann-Whitney U test was used to examine the differences between primary school students' and middle school students' networks. The results found that there is a bidirectional interaction between IA and anxiety, and anxiety plays a dominant role. Feeling afraid is the bridge symptom between IA and anxiety. IA did not show developmental stage differences, but anxiety did. These findings extend the model of compensatory internet use and suggest that, when alleviating IA symptoms in adolescents, attention should be given to their possible comorbid anxiety symptoms, especially in middle school students.

Prediction and validation of microbial community function from normal pulp to pulpitis caused by deep dentinal caries.

INTRODUCTION: Microbial function changes may be responsible for dental pulp transformation from normal to diseased. However, studies on the prediction and verification of the function of the microbial community in the deep dentine and pulp of caries-induced pulpitis are lacking. METHODS: This study included 171 cases of deep dentinal caries divided into normal pulp (NP), reversible pulpitis (RP), and irreversible pulpitis (IRP). In Experiment I, the microbial community composition was identified in 111 samples using 16S ribosomal DNA. Function prediction was performed through phylogenetic investigation of communities by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States prediction and qPCR. In Experiment II, different microbiome functions were confirmed in 60 samples using liquid chromatography-tandem mass spectrometry. RESULTS: In Experiment I, microbial abundance significantly differed in the IRP group compared to the other two groups. The RP and NP groups had the same microbiome composition, but the predicted functional difference between the RP and NP groups pertained to membrane transport (p < .010). The predicted functional difference between the IRP and NP groups pertained to amino-acid, co-factor, and vitamin metabolism (p < .010). In Experiment II, Kyoto Encyclopedia of Genes and Genomes functional annotation revealed that the differential metabolites between the RP and NP groups did not participate in membrane transport; however, the differential metabolites between the IRP and NP groups participated in amino-acid metabolism. CONCLUSIONS: The near-pulp microbiome in RP and NP with deep dentinal caries had the same differential function. However, amino acid metabolism in near the pulp microbial community differed between IRP and NP with deep dentinal caries.

A series of two-sample non-parametric tests for quantile residual life time.

Quantile residual lifetime (QRL) is of significant interest in many clinical studies as an easily interpretable quantity compared to other summary measures of survival distributions. In cancer or other chronic diseases, treatments are often compared based on the distributions or quantiles of the residual lifetime. Thus a common problem of interest is to test the equality of the QRL between two populations. In this paper, we propose two classes of tests to compare two QRLs; one class is based on the difference between two estimated QRLs, and the other is based on the estimating function of the QRL, where the estimated QRL from one sample is plugged into the QRL-estimating-function of the other sample. We outline the asymptotic properties of these test statistics. Simulation studies demonstrate that the proposed tests produced Type I errors closer to the nominal level and are superior to some existing tests based on both Type I error and power. Our proposed test statistics are also computationally less intensive and more straightforward compared to tests based on the confidence intervals. We applied the proposed methods to a randomized multicenter phase III trial for breast cancer patients.

Delayed-onset swelling around the implant after cochlear implantation: a series of 26 patients.

PURPOSE: We aimed to clarify the clinical features of delayed-onset swelling around cochlear implants (CI), and to present our experience on how to avoid and address this problem. METHODS: We performed a retrospective review of all CI cases at our institution between June 2001 and June 2020. Information on postoperative complications of swelling in the receiver area > 3 months after implantation were analyzed, and clinical data sheets were drawn. RESULTS: Twenty-six of 1425 patients (1.82%) with an age at implantation ranging from 1 to 9 years experienced delayed-onset swelling around the implant. Swelling episodes occurred as early as 4 months, and as late as 178 months after implantation (median, 79.7 months). The predisposing factor in 12 cases was unclear, 7 cases were caused by trauma at the implantation site, 5 cases were without predisposing factors, and 2 cases were related to infection. We found the frequency of delayed-onset swelling after cochlear implantation with different incision was statistically insignificant (P = 0.423). Nineteen patients (73.1%) were cured after one treatment, and five patients (19.2%) relapsed. Follow-up examinations at least 18 months after surgery revealed that all patients experienced a complete recovery. CONCLUSIONS: Delayed-onset swelling at the receiver site is a long term but not exactly uncommon complication after cochlear surgery and long-term follow-up is eagerly required. It can recur more than once, causing more complex treatment strategies in clinical practice. Conservative treatment first recommended, while needle aspiration should initially be considered in recurrent cases also when the effusion swelling is > 3 ml.

Comammox Nitrospira Clade B is the most abundant complete ammonia oxidizer in a dairy pasture soil and inhibited by dicyandiamide and high ammonium concentrations.

The recent discovery of comammox Nitrospira, a complete ammonia oxidizer, capable of completing the nitrification on their own has presented tremendous challenges to our understanding of the nitrification process. There are two divergent clades of comammox Nitrospira, Clade A and B. However, their population abundance, community structure and role in ammonia and nitrite oxidation are poorly understood. We conducted a 94-day microcosm study using a grazed dairy pasture soil amended with urea fertilizers, synthetic cow urine, and the nitrification inhibitor, dicyandiamide (DCD), to investigate the growth and community structure of comammox Nitrospira spp. We discovered that comammox Nitrospira Clade B was two orders of magnitude more abundant than Clade A in this fertile dairy pasture soil and the most abundant subcluster was a distinctive phylogenetic uncultured subcluster Clade B2. We found that comammox Nitrospira Clade B might not play a major role in nitrite oxidation compared to the role of canonical Nitrospira nitrite-oxidizers, however, comammox Nitrospira Clade B is active in nitrification and the growth of comammox Nitrospira Clade B was inhibited by a high ammonium concentration (700 kg synthetic urine-N ha-1) and the nitrification inhibitor DCD. We concluded that comammox Nitrospira Clade B: (1) was the most abundant comammox in the dairy pasture soil; (2) had a low tolerance to ammonium and can be inhibited by DCD; and (3) was not the dominant nitrite-oxidizer in the soil. This is the first study discovering a new subcluster of comammox Nitrospira Clade B2 from an agricultural soil.