BLISTER promotes seed maturation and fatty acid biosynthesis by interacting with WRINKLED1 to regulate chromatin dynamics in Arabidopsis.

WRINKLED1 (WRI1) is an important transcription factor that regulates seed oil biosynthesis. However, how WRI1 regulates gene expression during this process remains poorly understood. Here, we found that BLISTER (BLI) is expressed in maturing Arabidopsis thaliana seeds and acts as an interacting partner of WRI1. bli mutant seeds showed delayed maturation, a wrinkled seed phenotype, and reduced oil content, similar to the phenotypes of wri1. In contrast, BLI overexpression resulted in enlarged seeds and increased oil content. Gene expression and genetic analyses revealed that BLI plays a role in promoting the expression of WRI1 targets involved in fatty acid biosynthesis and regulates seed maturation together with WRI1. BLI is recruited by WRI1 to the AW boxes in the promoters of fatty acid biosynthesis genes. BLI shows a mutually exclusive interaction with the Polycomb-group protein CURLY LEAF (CLF) or the chromatin remodeling factor SWITCH/SUCROSE NONFERMENTING 3B (SWI3B), which facilitates gene expression by modifying nucleosomal occupancy and histone modifications. Together, these data suggest that BLI promotes the expression of fatty acid biosynthesis genes by interacting with WRI1 to regulate chromatin dynamics, leading to increased fatty acid production. These findings provide insights into the roles of the WRI1-BLI-CLF-SWI3B module in mediating seed maturation and gene expression.

The etiological diagnostic value of metagenomic next-generation sequencing in suspected community-acquired pneumonia.

BACKGROUND: The emergence of metagenomic next-generation sequencing (mNGS) may provide a promising tool for early and comprehensive identification of the causative pathogen in community-acquired pneumonia (CAP). In this study, we aim to further evaluate the etiological diagnostic value of mNGS in suspected CAP. METHODS: A total of 555 bronchoalveolar lavage fluid (BALF) samples were collected for pathogen detection by mNGS from 541 patients with suspected CAP. The clinical value was assessed based on infection diagnosis and treatment guidance. The diagnostic performance for pathogen identification by mNGS and sputum culture and for tuberculosis (TB) by mNGS and X-pert MTB/RIF were compared. To evaluate the potential for treatment guidance, we analyzed the treatment regimen of patients with suspected CAP, including imaging changes of lung after empirical antibacterial therapy, intensified regimen, antifungal treatment, and a 1-year follow up for patients with unconfirmed diagnosis and non-improvement imaging after anti-infective treatment and patients with high suspicion of TB or NTM infection who were transferred to the Wuhan Pulmonary Hospital for further diagnosis and even anti-mycobacterium therapy. RESULTS: Of the 516 BALF samples that were analyzed by both mNGS and sputum culture, the positivity rate of mNGS was significantly higher than that of sputum culture (79.1% vs. 11.4%, P = 0.001). A total of 48 samples from patients with confirmed TB were analyzed by both mNGS and X-pert MTB/RIF, and the sensitivity of mNGS for the diagnosis of active TB was significantly lower than that of X-pert MTB/RIF (64.6% vs. 85.4%, P = 0.031). Of the 106 pathogen-negative cases, 48 were ultimately considered non-infectious diseases, with a negative predictive value of 45.3%. Of the 381 pathogen-positive cases, 311 were eventually diagnosed as CAP, with a positive predictive value of 81.6%. A total of 487 patients were included in the evaluation of the therapeutic effect, and 67.1% improved with initial empirical antibiotic treatment. Of the 163 patients in which bacteria were detected, 77.9% improved with antibacterial therapy; of the 85 patients in which fungi were detected, 12.9% achieved remission after antifungal therapy. CONCLUSIONS: Overall, mNGS had unique advantages in the detection of suspected CAP pathogens. However, mNGS was not superior to X-pert MTB/RIF for the diagnosis of TB. In addition, mNGS was not necessary as a routine test for all patients admitted with suspected CAP. Furthermore, when fungi are detected by mNGS, antifungal therapy should be cautious.

An updated meta-analysis of effectiveness and safety of mTOR inhibitors in the management of tuberous sclerosis complex patients.

PURPOSE: Tuberculous sclerosis complex (TSC) is an autosomal dominant multi-system disease. In TSC patients, the inhibition of mTOR pathway is weakened, which leads to the uncontrolled proliferation of normal resting cells. Therefore, mTOR inhibitors have many therapeutic potentials in the treatment of TSC. However, there is no consensus on the safety and efficacy of mTOR inhibitors so far. This article aimed to present new evidence for the efficacy and safety of mTOR inhibitors in the treatment of TSC by evaluating published clinical trials. METHODS: A systemic search of online databases, such as Cochrane Library, Embase, PubMed, and the US National Institutes of Health Clinical Trials Registry, was conducted. The researchers selected studies that met the following entry criteria: randomized, double-blinded or single-blinded, placebo-controlled, parallel-group studies with active and control arms receiving rapamycin or everolimus and matched placebo, respectively. The meta-analysis included seven studies. Tumor response or epilepsy seizure frequency response rates were considered efficacy outcomes. RESULTS: In seven studies involving 877 patients, using of mTOR inhibitors therapy showed an improvement in both tumor response and seizure frequency outcomes in TSC. In combination of AML (angiomyolipomas), SEGA (subependymal giant cell astrocytoma), epilepsy, and facial angiofibroma subjects, the RR is 3.01 (95% CI 2.03 to 4.45, p = 0.000) with observed heterogeneity (I-squared = 55.4%). The main side effect of mTOR inhibitors was stomatitis. CONCLUSION: The updated meta-analysis suggests that the use of mTOR inhibitors is an effective therapy for patients with TSC.

Prostate cancer-associated transcript 6 (PCAT6) promotes epithelial-mesenchymal transition and stemness and worsens prognosis in patients with colorectal cancer.

Approximately 20% of colorectal cancer (CRC) patients are first diagnosed with metastatic colorectal cancer (mCRC) because they develop symptoms at an advanced stage. Despite advancements in treatment, patients with metastatic disease still experience inferior survival rates. Our objective is to investigate the association between long noncoding RNAs (lncRNAs) and prognosis and to explore their role in mCRC. In this study, we find that elevated expression of PCAT6 is independently linked to unfavourable survival outcomes in The Cancer Genome Atlas (TCGA) data, and this finding is further confirmed in CRC samples obtained from Fudan University Shanghai Cancer Center. Cell lines and xenograft mouse models are used to examine the impact of PCAT6 on tumor metastasis. Knockdown of PCAT6 is observed to impede the metastatic phenotype of CRC, as evidenced by functional assays, demonstrating the suppression of epithelial-mesenchymal transition (EMT) and stemness. Our findings show the significance of PCAT6 in mCRC and its potential use as a prognostic biomarker.

Immediate effect of kinesiology taping on muscle strength, static balance and proprioception after eccentric muscle fatigue on ankle: a randomized cross-over trial.

BACKGROUND: Kinesiology Taping(KT) is commonly used as a physical therapy to prevent exercise-induced fatigue. This study aims to evaluate the immediate effects of KT on muscle strength, static balance, and proprioception after eccentric muscle fatigue on ankle. METHODS: Twenty healthy male university students were recruited. The experimental protocol was structured into four sessions, each separated by a one-week washout period to prevent carryover effects. Participants were randomly allocated to one of four intervention conditions in each session, ensuring no participant received the same intervention twice. These conditions were: no taping(NT),sham taping(ST),athletic taping(AT),and kinesiology taping(KT).Taping was applied immediately following an eccentric muscle fatigue protocol targeting the ankle, and assessments were conducted in the order of proprioception, muscle strength and static balance. Isometric muscle strength and proprioception were evaluated using the Biodex isokinetic system. Static balance was measured using the TecnoBody balance platform. RESULTS: KT had a significantly higher plantarflexion/dorsiflexion peak torque, dorsiflexion average peak torque, and plantarflexion/dorsiflexion average power at 60°/s compared with NT and ST in terms of isometric muscle strength (p < 0.05).Furthermore, the plantarflexion peak torque of KT was significantly greater than AT at 60°/s[p = 0.005,95% confidence interval(CI) = 3.39 to 18.20] and 180°/s[p = 0.006,95%CI(2.62,21.98)]. In terms of proprioception, KT showed a lower absolute error in 25° plantarflexion and 10° dorsiflexion compared to NT, ST and AT. For static balance with eyes-open and eyes-closed conditions, AT and KT had a lower total sway area than NT and ST (p < 0.05). Additionally, a significant difference in total sway length with eyes-open condition was observed between AT and KT[p < 0.001,95%CI(-431.81,-168.25)];total sway area and the center of pressure(COP) velocity in the mediolateral(ML) and anteroposterior(AP) directions with eyes-closed condition were significantly lower in AT compared to KT. CONCLUSION: This study suggests that KT is more effective than other taping conditions in improving muscle strength and proprioception after eccentric muscle fatigue on ankle. However, AT is more helpful in increasing static postural control ability after ankle muscle fatigue than KT. TRIAL REGISTRATION: This study was registered with www.chictr.org.cn (registration number: ChiCTR2300068278) on 13/2/2023.

Partial-linear single-index transformation models with censored data.

In studies with time-to-event outcomes, multiple, inter-correlated, and time-varying covariates are commonly observed. It is of great interest to model their joint effects by allowing a flexible functional form and to delineate their relative contributions to survival risk. A class of semiparametric transformation (ST) models offers flexible specifications of the intensity function and can be a general framework to accommodate nonlinear covariate effects. In this paper, we propose a partial-linear single-index (PLSI) transformation model that reduces the dimensionality of multiple covariates into a single index and provides interpretable estimates of the covariate effects. We develop an iterative algorithm using the regression spline technique to model the nonparametric single-index function for possibly nonlinear joint effects, followed by nonparametric maximum likelihood estimation. We also propose a nonparametric testing procedure to formally examine the linearity of covariate effects. We conduct Monte Carlo simulation studies to compare the PLSI transformation model with the standard ST model and apply it to NYU Langone Health de-identified electronic health record data on COVID-19 hospitalized patients' mortality and a Veteran's Administration lung cancer trial.

State-specific fertility rate changes across the USA following the first two waves of COVID-19.

STUDY QUESTION: How did the first two coronavirus disease 2019 (COVID-19) waves affect fertility rates in the USA? SUMMARY ANSWER: States differed widely in how their fertility rates changed following the COVID-19 outbreak and these changes were influenced more by state-level economic, racial, political, and social factors than by COVID-19 wave severity. WHAT IS KNOWN ALREADY: The outbreak of the COVID-19 pandemic contributed to already declining fertility rates in the USA, but not equally across states. Identifying drivers of differential changes in fertility rates can help explain variations in demographic shifts across states in the USA and motivate policies that support families in general, not only during crises. STUDY DESIGN, SIZE, DURATION: This is an ecological study using state-level data from 50 US states and the District of Columbia (n = 51). The study period extends from 2020 to 2021 with historical data from 2016 to 2019. We identified Wave 1 as the first apex for each state after February 2020 and Wave 2 as the second apex, during Fall/Winter 2020-2021. PARTICIPANTS/MATERIALS, SETTING, METHODS: State-level COVID-19 wave severity, defined as case acceleration during each 3-month COVID-19 wave (cases/100 000 population/month), was derived from 7-day weekly moving average COVID-19 case rates from the US Centers for Disease Control and Prevention (CDC). State-level fertility rate changes (change in average monthly fertility rate/100 000 women of reproductive age (WRA)/year) were derived from the CDC Bureau of Vital Statistics and from 2020 US Census and University of Virginia 2021 population estimates 9 months after each COVID-19 wave. We performed univariate analyses to describe national and state-level fertility rate changes following each wave, and simple and multivariable linear regression analyses to assess the relation of COVID-19 wave severity and other state-level characteristics with fertility rate changes. MAIN RESULTS AND THE ROLE OF CHANCE: Nationwide, fertility dropped by 17.5 births/month/100 000 WRA/year following Wave 1 and 9.2 births/month/100 000 WRA/year following Wave 2. The declines following Wave 1 were largest among majority-Democrat, more non-White states where people practiced greater social distancing. Greater COVID-19 wave severity was associated with steeper fertility rate decline post-Wave 1 in simple regression, but the association was attenuated when adjusted for other covariates. Adjusting for the economic impact of the pandemic (hypothesized mediator) also attenuated the effect. There was no relation between COVID-19 wave severity and fertility rate change following Wave 2. LIMITATIONS, REASONS FOR CAUTION: Our study harnesses state-level data so individual-level conclusions cannot be inferred. There may be residual confounding in our multivariable regression and we were underpowered to detect some effects. WIDER IMPLICATIONS OF THE FINDINGS: The COVID-19 pandemic initially impacted the national fertility rate but, overall, the fertility rate rebounded to the pre-pandemic level following Wave 2. Consistent with prior literature, COVID-19 wave severity did not appear to predict fertility rate change. Economic, racial, political, and social factors influenced state-specific fertility rates during the pandemic more than the severity of the outbreak alone. Future studies in other countries should also consider whether these factors account for internal heterogeneity when examining the impact of the COVID-19 pandemic and other crises on fertility. STUDY FUNDING/COMPETING INTEREST(S): L.G.K. received funding from the National Institute of Environmental Health Sciences (R00ES030403), M.C. from the National Science Foundation Graduate Research Fellowship Program (20-A0-00-1005789), and M.L. and E.S. from the National Institute of Environmental Health Sciences (R01ES032808). None of the authors have competing interests. TRIAL REGISTRATION NUMBER: N/A.

Change-plane analysis for subgroup detection with a continuous treatment.

Detecting and characterizing subgroups with differential effects of a binary treatment has been widely studied and led to improvements in patient outcomes and population risk management. Under the setting of a continuous treatment, however, such investigations remain scarce. We propose a semiparametric change-plane model and consequently a doubly robust test statistic for assessing the existence of two subgroups with differential treatment effects under a continuous treatment. The proposed testing procedure is valid when either the baseline function for the covariate effects or the generalized propensity score function for the continuous treatment is correctly specified. The asymptotic distributions of the test statistic under the null and local alternative hypotheses are established. When the null hypothesis of no subgroup is rejected, the change-plane parameters that define the subgroups can be estimated. This paper provides a unified framework of the change-plane method to handle various types of outcomes, including the exponential family of distributions and time-to-event outcomes. Additional extensions with nonparametric estimation approaches are also provided. We evaluate the performance of our proposed methods through extensive simulation studies under various scenarios. An application to the Health Effects of Arsenic Longitudinal Study with a continuous environmental exposure of arsenic is presented.

Semiparametric distributed lag quantile regression for modeling time-dependent exposure mixtures.

Studying time-dependent exposure mixtures has gained increasing attentions in environmental health research. When a scalar outcome is of interest, distributed lag (DL) models have been employed to characterize the exposures effects distributed over time on the mean of final outcome. However, there is a methodological gap on investigating time-dependent exposure mixtures with different quantiles of outcome. In this paper, we introduce semiparametric partial-linear single-index (PLSI) DL quantile regression, which can describe the DL effects of time-dependent exposure mixtures on different quantiles of outcome and identify susceptible periods of exposures. We consider two time-dependent exposure settings: discrete and functional, when exposures are measured in a small number of time points and at dense time grids, respectively. Spline techniques are used to approximate the nonparametric DL function and single-index link function, and a profile estimation algorithm is proposed. Through extensive simulations, we demonstrate the performance and value of our proposed models and inference procedures. We further apply the proposed methods to study the effects of maternal exposures to ambient air pollutants of fine particulate and nitrogen dioxide on birth weight in New York University Children's Health and Environment Study (NYU CHES).

Goodness-of-fit two-phase sampling designs for time-to-event outcomes: a simulation study based on New York University Women's Health Study for breast cancer.

BACKGROUND: Sub-cohort sampling designs such as a case-cohort study play a key role in studying biomarker-disease associations due to their cost effectiveness. Time-to-event outcome is often the focus in cohort studies, and the research goal is to assess the association between the event risk and risk factors. In this paper, we propose a novel goodness-of-fit two-phase sampling design for time-to-event outcomes when some covariates (e.g., biomarkers) can only be measured on a subgroup of study subjects. METHODS: Assuming that an external model, which can be the well-established risk models such as the Gail model for breast cancer, Gleason score for prostate cancer, and Framingham risk models for heart diseases, or built from preliminary data, is available to relate the outcome and complete covariates, we propose to oversample subjects with worse goodness-of-fit (GOF) based on an external survival model and time-to-event. With the cases and controls sampled using the GOF two-phase design, the inverse sampling probability weighting method is used to estimate the log hazard ratio of both incomplete and complete covariates. We conducted extensive simulations to evaluate the efficiency gain of our proposed GOF two-phase sampling designs over case-cohort study designs. RESULTS: Through extensive simulations based on a dataset from the New York University Women's Health Study, we showed that the proposed GOF two-phase sampling designs were unbiased and generally had higher efficiency compared to the standard case-cohort study designs. CONCLUSION: In cohort studies with rare outcomes, an important design question is how to select informative subjects to reduce sampling costs while maintaining statistical efficiency. Our proposed goodness-of-fit two-phase design provides efficient alternatives to standard case-cohort designs for assessing the association between time-to-event outcome and risk factors. This method is conveniently implemented in standard software.

Characterisation of personalised air pollution exposure in pregnant women participating in a birth cohort study.

BACKGROUND: Air pollution is a health risk in pregnant women and children. Despite the importance of refined exposure assessment, the characterisation of personalised air pollution exposure remains a challenge in paediatric and perinatal epidemiology. OBJECTIVE: We used portable personal air monitors to characterise personalised exposure to air pollutants in pregnant women. METHODS: Between November 2019 and May 2022, we offered personal air monitors to pregnant women participating in a birth cohort in New York City. During pregnancy, women used air monitors, which measured particulate matter (PM), nitrogen dioxide (NO2 ), and volatile organic compounds (average use = 14 days). Data were stored in real-time on a secure database via synchronisation with a smartphone application. Of 497 women who agreed to use air monitors, 273 women (55%) were successful in using air monitors for longer than a day. For these participants, we identified daily patterns of exposure to air pollutants using functional principal component analysis (3827 days of air monitoring). RESULTS: Compared to women with no pollution data (n = 224), women who successfully used monitors were more likely to be non-Hispanic White and Asian (vs. Hispanic), nulliparous, unemployed, married/partnered, and received the device in-person (vs. mailed). We identified different daily patterns of exposure to air pollutants. The most dominant pattern for all pollutants was low exposure levels with little variations within 24 h, followed by a pattern that showed differences between day and night levels. NO2 had higher daily variations compared to PM. CONCLUSIONS: Small wearables are useful for the measurement of personalised air pollution exposure in birth cohorts and identify daily patterns that cannot be captured otherwise. Successful participation, however, depends on certain individual characteristics. Future studies should consider strategies in design and analysis to account for selective participation.

Negative correlation between acetyl-CoA acyltransferase 2 and cetuximab resistance in colorectal cancer.

The emergence of anti-EGFR therapy has revolutionized the treatment of colorectal cancer (CRC). However, not all patients respond consistently well. Therefore, it is imperative to conduct further research to identify the molecular mechanisms underlying the development of cetuximab resistance in CRC. In this study, we find that the expressions of many metabolism-related genes are downregulated in cetuximab-resistant CRC cells compared to their sensitive counterparts. Specifically, acetyl-CoA acyltransferase 2 (ACAA2), a key enzyme in fatty acid metabolism, is downregulated during the development of cetuximab resistance. Silencing of ACAA2 promotes proliferation and increases cetuximab tolerance in CRC cells, while overexpression of ACAA2 exerts the opposite effect. RTK-Kras signaling might contribute to the downregulation of ACAA2 expression in CRC, and ACAA2 predicts CRC prognosis in patients with Kras mutations. Collectively, our data suggest that modulating ACAA2 expression contributes to secondary cetuximab resistance in Kras wild-type CRC patients. ACAA2 expression is related to Kras mutation and demonstrates a prognostic role in CRC patients with Kras mutation. Thus, ACAA2 is a potential target in CRC with Kras mutation.

JUNB mediates oxaliplatin resistance via the MAPK signaling pathway in gastric cancer by chromatin accessibility and transcriptomic analysis.

Currently, platinum-containing regimens are the most commonly used regimens for advanced gastric cancer patients, and chemotherapy resistance is one of the main reasons for treatment failure. Thus, it is important to reveal the mechanism of oxaliplatin resistance and to seek effective intervention strategies to improve chemotherapy sensitivity, thereby improving the survival and prognosis of gastric cancer patients. To understand the molecular mechanisms of oxaliplatin resistance, we generate an oxaliplatin-resistant gastric cancer cell line and conduct assay for transposase-accessible chromatin sequencing (ATAC-seq) and RNA sequencing (RNA-seq) for both parental and oxaliplatin-resistant AGS cells. A total of 3232 genomic regions are identified to have higher accessibility in oxaliplatin-resistant cells, and DNA-binding motif analysis identifies JUNB as the core transcription factor in the regulatory network. JUNB is overexpressed in oxaliplatin-resistant gastric cancer cells, and its upregulation is associated with poor prognosis in gastric cancer patients, which is validated by our tissue microarray data. Moreover, chromatin immunoprecipitation sequencing (ChIP-seq) analysis reveals that JUNB binds to the transcriptional start site of key genes involved in the MAPK signaling pathway. Knockdown of JUNB inhibits the MAPK signaling pathway and restores sensitivity to oxaliplatin. Combined treatment with the ERK inhibitor piperlongumine or MEK inhibitor trametinib effectively overcomes oxaliplatin resistance. This study provides evidence that JUNB mediates oxaliplatin resistance in gastric cancer by activating the MAPK pathway. The combination of MAPK inhibitors with oxaliplatin overcomes resistance to oxaliplatin, providing a promising treatment opportunity for oxaliplatin-resistant gastric cancer patients.

The Effect of Maternal US Nativity on Racial/Ethnic Differences in Fetal Growth.

While racial/ethnic differences in fetal growth have been documented, few studies have examined whether they vary by exogenous factors, which could elucidate underlying causes. The purpose of this study was to characterize longitudinal fetal growth patterns by maternal sociodemographic, behavioral, and clinical factors and examine whether associations with maternal race/ethnicity varied by these other predictors. Between 2016 and 2019, pregnant women receiving prenatal care at NYU Langone Health (New York, New York) were invited to participate in a birth cohort study. Women completed questionnaires, and clinical data were abstracted from ultrasound examinations. Maternal characteristics were assessed in relation to fetal biometric measures throughout pregnancy using linear mixed models. Maternal race/ethnicity was consistently associated with fetal biometry: Black, Hispanic, and Asian women had fetuses with smaller head circumference, abdominal circumference, and biparietal diameter than White women. The associations between race/ethnicity and fetal growth varied by nativity for Asian women, such that the disparity between Asian and White women was much greater for US-born women than for foreign-born women. However, associations for Black and Hispanic women did not vary by nativity. While race/ethnicity-specific fetal growth standards have been proposed, additional work is needed to elucidate what could be driving these differences, including factors that occur in parallel and differentially affect fetal growth.

Longitudinal associations of pre-pregnancy BMI and gestational weight gain with maternal urinary metabolites: an NYU CHES study.

BACKGROUND/OBJECTIVES: Excessive gestational weight gain (GWG) and pre-pregnancy obesity affect a significant portion of the US pregnant population and are linked with negative maternal and child health outcomes. The objective of this study was to explore associations of pre-pregnancy body mass index (pBMI) and GWG with longitudinally measured maternal urinary metabolites throughout pregnancy. SUBJECTS/METHODS: Among 652 participants in the New York University Children's Health and Environment Study, a longitudinal pregnancy cohort, targeted metabolomics were measured in serially collected urine samples throughout pregnancy. Metabolites were measured at median 10 (T1), 21 (T2), and 29 (T3) weeks gestation using the Biocrates AbsoluteIDQ® p180 Urine Extension kit. Acylcarnitine, amino acid, biogenic amine, phosphatidylcholine, lysophosphatidylcholine, sphingolipid, and sugar levels were quantified. Pregnant people 18 years or older, without type 1 or 2 diabetes and with singleton live births and valid pBMI and metabolomics data were included. GWG and pBMI were calculated using weight and height data obtained from electronic health records. Linear mixed effects models with interactions with time were fit to determine the gestational age-specific associations of categorical pBMI and continuous interval-specific GWG with urinary metabolites. All analyses were corrected for false discovery rate. RESULTS: Participants with obesity had lower long-chain acylcarnitine levels throughout pregnancy and lower phosphatidylcholine and glucogenic amino acids and higher phenylethylamine concentrations in T2 and T3 compared with participants with normal/underweight pBMI. GWG was associated with taurine in T2 and T3 and C5 acylcarnitine species, C5:1, C5-DC, and C5-M-DC, in T2. CONCLUSIONS: pBMI and GWG were associated with the metabolic environment of pregnant individuals, particularly in relation to mid-pregnancy. These results highlight the importance of both preconception and prenatal maternal health.

Mechanistic Study on the Species Differences in Excretion Pathway of HR011303 in Humans and Rats.

Excretion of [14C]HR011303-derived radioactivity showed significant species difference. Urine (81.50% of dose) was the main excretion route in healthy male subjects, whereas feces (87.16% of dose) was the main excretion route in rats. To further elucidate the underlying cause for excretion species differences of HR011303, studies were conducted to uncover its metabolism and excretion mechanism. M5, a glucuronide metabolite of HR011303, is the main metabolite in humans and rats. Results of a rat microsome incubation study suggested that HR011303 was metabolized to M5 in the rat liver. According to previous studies, M5 is produced in both human liver and kidney microsomes. We found that M5 in the human liver can be transported to the blood by multidrug resistance-associated protein (MRP) 3, and then the majority of M5 can be hydrolyzed to HR011303. HR011303 enters the human kidney or liver through passive diffusion, whereas M5 is taken up through organic anion transporter (OAT) 3, organic anion-transporting polypeptide (OATP) 1B1, and OATP1B3. When HR011303 alone is present, it can be metabolized to M5 in both sandwich-cultured rat hepatocytes (SCRH) and sandwich-cultured human hepatocytes (SCHH) and excreted into bile as M5 in SCRH. Using transporter inhibitors in sandwich-cultured model and membrane vesicles expressing MRP2 or Mrp2, we found that M5 was a substance of MRP2/Mrp2, and the bile efflux of M5 was mainly mediated by MRP2/Mrp2. Considering the significant role of MRP3/Mrp3 and MRP2/Mrp2 in the excretion of glucuronides, the competition between them for M5 was possibly the determinant for the different excretion routes in humans and rats. SIGNIFICANCE STATEMENT: Animal experiments are necessary to predict dosage and safety of candidate drugs prior to clinical trials. However, extrapolation results often differ from the actual situation. For HR011303, excretory pathways exhibited a complete reversal, through urine in humans and feces in rats. Such phenomena have been observed in several drugs, but no in-depth studies have been conducted to date. In the present study, the excretion species differences of HR011303 can be explained by the competition for M5 between MRP2/Mrp2 and MRP3/Mrp3.

Pharmacokinetics, Mass Balance, and Metabolism of the Novel Urate Transporter 1 Inhibitor [14C]HR011303 in Humans: Metabolism Is Mediated Predominantly by UDP-Glucuronosyltransferase.

HR011303, a promising selective urate transporter 1 inhibitor, is currently being studied in a phase III clinical trial in China for the treatment of hyperuricemia and gout. In the current study, the pharmacokinetics, mass balance, and metabolism of HR011303 were examined in six healthy Chinese male subjects who received a single oral dose of 10 mg of [14C]HR011303 (80 µCi). The results showed that HR011303 was rapidly absorbed with a median time to reach C max of 1.50 hours postdose, and the arithmetic mean half-life of total radioactivity was approximately 24.2 hours in plasma. The mean blood-to-plasma radioactivity concentration ratio was 0.66, suggesting the preferential distribution of drug-related components in plasma. At 216 hours postdose, the mean cumulative excreted radioactivity was 91.75% of the dose, including 81.50% in urine and 10.26% in feces. Six metabolites were identified, and the parent drug HR011303 was the most abundant component in plasma and feces, but a minor component in urine. Glucuronidation of the carboxylic acid moiety of HR011303 was the primary metabolic pathway in humans, amounting to 69.63% of the dose (M5, 51.57% of the dose; M5/2, 18.06% of the dose) in the urine; however, it was not detected in plasma. UDP-glucuronosyltransferase (UGT) 2B7 was responsible for the formation of M5. Overall, after a single oral dose of 10 mg of [14C]HR011303 (80 µCi), HR011303 and its main metabolites were eliminated via renal excretion. The major metabolic pathway was carboxylic acid glucuronidation, which was catalyzed predominantly by UGT2B7. SIGNIFICANCE STATEMENT: This study determined the absorption and disposition of HR011303, a selective urate transporter (URAT) 1 inhibitor currently in development for the treatment of hyperuricemia and gout. This work helps to characterize the major metabolic pathways of new URAT inhibitors and identify the absorption and clearance mechanism.

COVID-19 outcomes of patients with gynecologic cancer in New York City: An updated analysis from the initial surge of the pandemic.

BACKGROUND: Despite significant increase in COVID-19 publications, characterization of COVID-19 infection in patients with gynecologic cancer remains limited. Here we present an update of COVID-19 outcomes among people with gynecologic cancer in New York City (NYC) during the initial surge of severe acute respiratory syndrome coronavirus 2 (coronavirus disease 2019 [COVID-19]). METHODS: Data were abstracted from gynecologic oncology patients with COVID-19 infection among 8 NYC area hospital systems between March and June 2020. Multivariable logistic regression was utilized to estimate associations between factors and COVID-19 related hospitalization and mortality. RESULTS: Of 193 patients with gynecologic cancer and COVID-19, the median age at diagnosis was 65.0 years (interquartile range (IQR), 53.0-73.0 years). One hundred six of the 193 patients (54.9%) required hospitalization; among the hospitalized patients, 13 (12.3%) required invasive mechanical ventilation, 39 (36.8%) required ICU admission. Half of the cohort (49.2%) had not received anti-cancer treatment prior to COVID-19 diagnosis. No patients requiring mechanical ventilation survived. Thirty-four of 193 (17.6%) patients died of COVID-19 complications. In multivariable analysis, hospitalization was associated with an age ≥ 65 years (odds ratio [OR] 2.12, 95% confidence interval [CI] 1.11, 4.07), Black race (OR 2.53, CI 1.24, 5.32), performance status ≥2 (OR 3.67, CI 1.25, 13.55) and ≥ 3 comorbidities (OR 2.00, CI 1.05, 3.84). Only former or current history of smoking (OR 2.75, CI 1.21, 6.22) was associated with death due to COVID-19 in multivariable analysis. Administration of cytotoxic chemotherapy within 90 days of COVID-19 diagnosis was not predictive of COVID-19 hospitalization (OR 0.83, CI 0.41, 1.68) or mortality (OR 1.56, CI 0.67, 3.53). CONCLUSIONS: The case fatality rate among patients with gynecologic malignancy with COVID-19 infection was 17.6%. Cancer-directed therapy was not associated with an increased risk of mortality related to COVID-19 infection.

PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation.

Biomarkers predict World Trade Center-Lung Injury (WTC-LI); however, there remains unaddressed multicollinearity in our serum cytokines, chemokines, and high-throughput platform datasets used to phenotype WTC-disease. To address this concern, we used automated, machine-learning, high-dimensional data pruning, and validated identified biomarkers. The parent cohort consisted of male, never-smoking firefighters with WTC-LI (FEV1, %Pred< lower limit of normal (LLN); n = 100) and controls (n = 127) and had their biomarkers assessed. Cases and controls (n = 15/group) underwent untargeted metabolomics, then feature selection performed on metabolites, cytokines, chemokines, and clinical data. Cytokines, chemokines, and clinical biomarkers were validated in the non-overlapping parent-cohort via binary logistic regression with 5-fold cross validation. Random forests of metabolites (n = 580), clinical biomarkers (n = 5), and previously assayed cytokines, chemokines (n = 106) identified that the top 5% of biomarkers important to class separation included pigment epithelium-derived factor (PEDF), macrophage derived chemokine (MDC), systolic blood pressure, macrophage inflammatory protein-4 (MIP-4), growth-regulated oncogene protein (GRO), monocyte chemoattractant protein-1 (MCP-1), apolipoprotein-AII (Apo-AII), cell membrane metabolites (sphingolipids, phospholipids), and branched-chain amino acids. Validated models via confounder-adjusted (age on 9/11, BMI, exposure, and pre-9/11 FEV1, %Pred) binary logistic regression had AUCROC [0.90(0.84-0.96)]. Decreased PEDF and MIP-4, and increased Apo-AII were associated with increased odds of WTC-LI. Increased GRO, MCP-1, and simultaneously decreased MDC were associated with decreased odds of WTC-LI. In conclusion, automated data pruning identified novel WTC-LI biomarkers; performance was validated in an independent cohort. One biomarker-PEDF, an antiangiogenic agent-is a novel, predictive biomarker of particulate-matter-related lung disease. Other biomarkers-GRO, MCP-1, MDC, MIP-4-reveal immune cell involvement in WTC-LI pathogenesis. Findings of our automated biomarker identification warrant further investigation into these potential pharmacotherapy targets.

Determinants of phthalate exposures in pregnant women in New York City.

Previous studies have provided data on determinants of phthalates in pregnant women, but results were disparate across regions. We aimed to identify the food groups and demographic factors that predict phthalate exposure in an urban contemporary pregnancy cohort in the US. The study included 450 pregnant women from the New York University Children's Health and Environment Study in New York City. Urinary concentrations of 22 phthalate metabolites, including metabolites of di-2-ethylhexylphthalate (DEHP), were determined at three time points across pregnancy by liquid chromatography coupled with tandem mass spectrometry. The Diet History Questionnaire II was completed by pregnant women at mid-pregnancy to assess dietary information. Linear mixed models were fitted to examine determinants of urinary phthalate metabolite concentrations. Using partial-linear single-index (PLSI) models, we assessed the major contributors, among ten food groups, to phthalate exposure. Metabolites of DEHP and its ortho-phthalate replacement, diisononyl phthalate (DiNP), were found in >90% of the samples. The sum of creatinine-adjusted DiNP metabolite concentrations was higher in older and single women and in samples collected in summer. Hispanic and non-Hispanic Black women had lower urinary concentrations of summed metabolites of di-n-octyl phthalate (DnOP), but higher concentrations of low molecular weight phthalates compared with non-Hispanic White women. Each doubling of grain products consumed was associated with a 20.9% increase in ∑DiNP concentrations (95%CI: 4.5, 39.9). PLSI models revealed that intake of dried beans and peas was the main dietary factor contributing to urinary ∑DEHP, ∑DiNP, and ∑DnOP levels, with contribution proportions of 76.3%, 35.8%, and 27.4%, respectively. Urinary metabolite levels of phthalates in pregnant women in NYC varied by age, marital status, seasonality, race/ethnicity, and diet. These results lend insight into the major determinants of phthalates levels, and may be used to identify exposure sources and guide interventions to reduce exposures in susceptible populations.