A scalable, high-throughput neural development platform identifies shared impact of ASD genes on cell fate and differentiation.

Background: Deleterious mutations in hundreds of genes confer high risk for neurodevelopmental disorders (NDDs), posing significant challenges for therapeutic development. Identifying convergent pathways shared across NDD genes could reveal high-impact therapeutic targets. Methods: To identity convergent pathways in NDD genes, we optimized Perturb-seq, a method combining CRISPR perturbation with single-cell RNA sequencing (scRNA-seq), and applied structural topic modeling (STM) to simultaneously assess impact on cell fate and developmental stage. We then studied a subset of autism spectrum disorder (ASD) genes implicated in regulation of gene expression using these improved molecular and analytical approaches. Results: Results from targeting 60 high-confidence ASD risk genes revealed significant effects on neural development. As expected, ASD risk genes impacted both progenitor fate and/or neuronal differentiation. Using STM, we could identify latent topics jointly capturing cell types, cell fate, and differentiation stages. Repression of ASD risk genes led to changes in topic proportions and effects of four genes (DEAF1, KMT2A, MED13L, and MYT1L) were validated in an independent dataset. Conclusions: Our optimized Perturb-seq method, combined with a novel analytical approach, provides a powerful, cost-effective framework for uncovering convergent mechanisms among genes involved in complex neurodevelopmental processes. Application of these methods advanced understanding of the impact of ASD mutations on multiple dimensions of neural development, and provides a framework for a broader examination of the function of NDD risk genes.

Comparisons between the Direct and Indirect Effect of 1.5 keV X-rays and 0-30 eV Electrons on DNA: Base Lesions, Stand Breaks, Cross-Links, and Cluster Damages.

The interaction of low energy electrons (LEEs; 1-30 eV) with genomic material can induce multiple types of damage that may cause the loss of genetic information, mutations, genome instability, and cell death. For all damages measurable by electrophoresis, we provide the first complete set of G-values (yield of a specific product per energy deposited) induced in plasmid DNA by the direct and indirect effects of LEEs (GLEE) and 1.5 keV X-rays (GX) under identical conditions. Low energy photoelectrons are produced via X-rays incident on a tantalum (Ta) substrate covered with DNA and placed in a chamber filled with nitrogen at atmospheric pressure, under four different humidity levels, ranging from dry conditions to full hydration (Γ = 2.5 to Γ = 33, where Γ is the number of water molecules/nucleotide). Damage yields are measured as a function of X-ray fluence and humidity. GLEE values are between 2 and 27 times larger than those for X-rays. At Γ = 2.5 and 33, GLEE values for double strand breaks are 27 and 16 times larger than GX, respectively. The indirect effect contributes ∼50% to the total damage. These G-values allow quantification of potentially lethal lesions composed of strand breaks and/or base damages in the presence of varying amounts of water, i.e., closer to cellular conditions.

Sex-Differential Gene Expression in Developing Human Cortex and Its Intersection With Autism Risk Pathways.

Background: Sex-differential biology may contribute to the consistently male-biased prevalence of autism spectrum disorder (ASD). Gene expression differences between males and females in the brain can indicate possible molecular and cellular mechanisms involved, although transcriptomic sex differences during human prenatal cortical development have been incompletely characterized, primarily due to small sample sizes. Methods: We performed a meta-analysis of sex-differential expression and co-expression network analysis in 2 independent bulk RNA sequencing datasets generated from cortex of 273 prenatal donors without known neuropsychiatric disorders. To assess the intersection between neurotypical sex differences and neuropsychiatric disorder biology, we tested for enrichment of ASD-associated risk genes and expression changes, neuropsychiatric disorder risk genes, and cell type markers within identified sex-differentially expressed genes (sex-DEGs) and sex-differential co-expression modules. Results: We identified 101 significant sex-DEGs, including Y-chromosome genes, genes impacted by X-chromosome inactivation, and autosomal genes. Known ASD risk genes, implicated by either common or rare variants, did not preferentially overlap with sex-DEGs. We identified 1 male-specific co-expression module enriched for immune signaling that is unique to 1 input dataset. Conclusions: Sex-differential gene expression is limited in prenatal human cortex tissue, although meta-analysis of large datasets allows for the identification of sex-DEGs, including autosomal genes that encode proteins involved in neural development. Lack of sex-DEG overlap with ASD risk genes in the prenatal cortex suggests that sex-differential modulation of ASD symptoms may occur in other brain regions, at other developmental stages, or in specific cell types, or may involve mechanisms that act downstream from mutation-carrying genes.

Males are more commonly diagnosed with autism spectrum disorder than females, and sex differences in brain development may contribute to this difference. Here, we define differences in gene expression patterns between males and females in human prenatal brain tissue from 273 donors to identify 101 genes that are expressed at different levels in males and females and gene sets that show sex-specific expression correlations. Genes with autism-associated DNA variants and genes with altered expression in autism do not preferentially overlap with sex-differential genes, suggesting that sex-differential biology may influence autism risk mechanisms in other brain regions, at other developmental stages, or in specific cell types.

The Renaissance of Poly(3-hexylthiophene) as a Promising Hole-Transporting Material Toward Efficient and Stable Perovskite Solar Cells.

To push the commercialization of the promising photovoltaic technique of perovskite solar cells (PSCs), the three-element golden law of efficiency, stability, and cost should be followed. As the key component of PSCs, hole-transporting materials (HTMs) involving widely-used organic semiconductors such as 2,2',7,7'-tetrakis-(N,N-di-4-methoxyphenylamino)-9,9'-spirobifluorene (Spiro-OMeTAD) or poly(triarylamine) (PTAA) usually suffer high-cost preparation and low operational stability. Fortunately, the studies on the classical p-type polymer poly(3-hexylthiophene) (P3HT) as an alternative HTM have recently sparked a broad interest due to its low-cost synthesis, excellent batch-to-batch purity, superior hole conductivity as well as controllable and stable film morphology. Despite this, the device efficiency still lags behind P3HT-based PSCs mainly owing to the mismatched energy level and poor interfacial contact between P3HT and the perovskite layer. Hence, in this review, the study timely summarizes the developed strategies for overcoming the corresponding issues such as interface engineering, morphology regulation, and formation of composite HTMs from which some critical clues can be extracted to provide guidance for further boosting the efficiency and stability of P3HT-based devices. Finally, in the outlook, the future research directions either from the viewpoint of material design or device engineering are outlined.

De Novo Design of Spiro-Type Hole-Transporting Material: Anisotropic Regulation Toward Efficient and Stable Perovskite Solar Cells.

2,2',7,7'-Tetrakis(N,N-di-p-methoxyphenyl)-amine-9,9'-spirobifluorene (Spiro-OMeTAD) represents the state-of-the-art hole-transporting material (HTM) in n-i-p perovskite solar cells (PSCs). However, its susceptibility to stability issues has been a long-standing concern. In this study, we embark on a comprehensive exploration of the untapped potential within the family of spiro-type HTMs using an innovative anisotropic regulation strategy. Diverging from conventional approaches that can only modify spirobifluorene with single functional group, this approach allows us to independently tailor the two orthogonal components of the spiro-skeleton at the molecular level. The newly designed HTM, SF-MPA-MCz, features enhanced thermal stability, precise energy level alignment, superior film morphology, and optimized interfacial properties when compared to Spiro-OMeTAD, which contribute to a remarkable power conversion efficiency (PCE) of 24.53% for PSCs employing SF-MPA-MCz with substantially improved thermal stability and operational stability. Note that the optimal concentration for SF-MPA-MCz solution is only 30 mg/ml, significantly lower than Spiro-OMeTAD (>70 mg/ml), which could remarkably reduce the cost especially for large-area processing in future commercialization. This work presents a promising avenue for the versatile design of multifunctional HTMs, offering a blueprint for achieving efficient and stable PSCs.

Anchoring of Fe-MIL-101-NH2 to the Polymer Membrane Matrix through the Hinsberg Reaction to Promote Conductivity of SPEEK Membranes.

SCPEEK@MOF proton exchange membranes, where SCPEEK is sulfinyl chloride polyether ether ketone and MOF is a metal-organic framework, were prepared by doping Fe-MIL-101-NH2 into polymers. The amino group in the MOF and the -SOCl2 group in thionyl chloride polyether ether ketone cross-link to form a covalent bond through the Hinsberg reaction, and the prepared composite membrane has stronger stability than other electrostatic interactions and simple physical doping composite membranes. The formation of covalent bonds improves the water absorption of the composite membrane, which makes it easy for water molecules to form hydrogen bonds. Moreover, SPEEK as a proton conductive polymer and the synergy of MOFs improve the proton conductivity of composite membranes. The composite membranes were characterized by Fourier transform infrared spectroscopy, powder X-ray diffraction, scanning electron microscopy, and atomic force microscopy. The swelling rate, water absorption, mechanical stability, ion exchange capacity, and proton conductivity of the pure sulfonated polyether ether ketone (SPEEK) membrane were compared with those of the mechanically doped SPEEK/MOF membrane and the composite membrane SCPEEK@MOF doped with different ratios of Fe-MIL-101-NH2, and all of the SCPEEK@MOF showed superior performance. When the Fe-MIL-101-NH2 loading rate of the composite membrane is 2%, the proton conductivity of the composite membrane can reach 0.202 S cm-1 at 363 K and a 98% relative humidity, which is much higher than that of the SPEEK/MOF membrane obtained by simple physical doping under the same conditions.

Identification of pneumococcal serotypes with individual recognition of vaccine types by a highly multiplexed real-time PCR-based MeltArray approach.

BACKGROUND: Pneumococcus serotyping is important for monitoring serotype epidemiology, vaccine-induced serotypes replacement and emerging pathogenic serotypes. However, the lack of high-resolution serotyping tools has hindered its widespread implementation. METHODS: We devised a single-step, multiplex real-time polymerase chain reaction (PCR)-based MeltArray approach termed PneumoSero that can identify 92 serotypes with individual recognition of 54 serotypes, including all 24 currently available vaccine types. The limit of detection (LOD) and the ability to coexisting serotypes were studied, followed by analytical evaluation using 92 reference pneumococcal strains and 125 non-pneumococcal strains, and clinical evaluation using 471 pneumococcus isolates and 46 pneumococcus-positive clinical samples. RESULTS: The LODs varied with serotypes from 50 to 100 copies per reaction and 10 % of the minor serotypes were detectable in samples containing two mixed serotypes. Analytical evaluation presented 100 % accuracy in both 92 reference pneumococcal strains and 125 non-pneumococcal strains. Clinical evaluation of 471 pneumococcus isolates displayed full concordance with Sanger sequencing results. The 46 clinical specimens yielded 45 typeable results and one untypeable result. Of the 45 typeable samples, 41 were of a single serotype and four were of mixed serotypes, all of which were confirmed by Sanger sequencing or separate PCR assays. CONCLUSION: We conclude that the PneumoSero assay can be implemented as a routine tool for pneumococcal serotyping in standard microbiology laboratories and even in clinical settings.

DNA Protection against Damages Induced by Low-Energy Electrons: Absolute Cross Sections for Arginine-DNA Complexes.

Histone proteins protect cellular DNA from radiation damage. We find that arginine, a major component of histone proteins, protects DNA from lesions induced by low-energy secondary electrons generated by radiation. Thin films of 7 ± 2, 12 ± 4, and 17 ± 4 nm thicknesses containing arginine-plasmid-DNA complexes in molar ratio of [Arg2+]/[PO4-] = 16 are irradiated in vacuum with 5 and 10 eV electrons. Damage yields are measured for base damages, cross-links, single-strand breaks (SSBs), double-strand breaks, and other clustered lesions. Most damage results from dissociative electron attachment. Absolute cross sections (ACSs) for all damage types are extracted from yields at different film thicknesses. Compared with bare DNA, these ACSs are reduced by factors of up to 4.4 in Arg-DNA complexes. SSB protection is the highest. Potentially lethal cluster lesions decrease by factors of up to 2.2. ACSs are critical input parameters in modeling radiation-induced damage and assessing protection factors under simulated cellular conditions.

MuSiC2: cell-type deconvolution for multi-condition bulk RNA-seq data.

Cell-type composition of intact bulk tissues can vary across samples. Deciphering cell-type composition and its changes during disease progression is an important step toward understanding disease pathogenesis. To infer cell-type composition, existing cell-type deconvolution methods for bulk RNA sequencing (RNA-seq) data often require matched single-cell RNA-seq (scRNA-seq) data, generated from samples with similar clinical conditions, as reference. However, due to the difficulty of obtaining scRNA-seq data in diseased samples, only limited scRNA-seq data in matched disease conditions are available. Using scRNA-seq reference to deconvolve bulk RNA-seq data from samples with different disease conditions may lead to a biased estimation of cell-type proportions. To overcome this limitation, we propose an iterative estimation procedure, MuSiC2, which is an extension of MuSiC, to perform deconvolution analysis of bulk RNA-seq data generated from samples with multiple clinical conditions where at least one condition is different from that of the scRNA-seq reference. Extensive benchmark evaluations indicated that MuSiC2 improved the accuracy of cell-type proportion estimates of bulk RNA-seq samples under different conditions as compared with the traditional MuSiC deconvolution. MuSiC2 was applied to two bulk RNA-seq datasets for deconvolution analysis, including one from human pancreatic islets and the other from human retina. We show that MuSiC2 improves current deconvolution methods and provides more accurate cell-type proportion estimates when the bulk and single-cell reference differ in clinical conditions. We believe the condition-specific cell-type composition estimates from MuSiC2 will facilitate the downstream analysis and help identify cellular targets of human diseases.

Rapid detection of the irinotecan-related UGT1A1*28 polymorphism by asymmetric PCR melting curve analysis using one fluorescent probe.

BACKGROUND: Determination of UGT1A1 (TA)n polymorphism prior to irinotecan therapy is necessary to avoid severe adverse drug effects. Thus, accurate and reliable genotyping methods for (TA)n polymorphism are highly desired. Here, we present a new method for polymerase chain reaction (PCR) melting curve analysis using one fluorescent probe to discriminate the UGT1A1*1 [(TA)6 ] and *28 [(TA)7 ] genotypes. METHODS: After protocol optimization, this technique was applied for genotyping of 64 patients (including 23 with UGT1A1*1/*1, 22 with *1/*28, and 19 with *28/*28) recruited between 2016 and 2021 in China-Japan Friendship Hospital. The accuracy of the method was evaluated by comparing the results with those of direct sequencing and fragment analysis. The intra- and inter-run precision of the melting temperatures (Tm s) were calculated to assess the reliability, and the limit of detection was examined to assess the sensitivity. RESULTS: All genotypes were correctly identified with the new method, and its accuracy was higher than that of fragment analysis. The intra- and inter-run coefficients of variation for the Tm s were both ≤0.27%, with standard deviations ≤0.14°C. The limit of detection was 0.2 ng of input genomic DNA. CONCLUSION: The developed PCR melting curve analysis using one fluorescent probe can provide accurate, reliable, rapid, simple, and low-cost detection of UGT1A1 (TA)n polymorphism, and its use can be easily generalized in clinical laboratories with a fluorescent PCR platform.

Constructing local cell-specific networks from single-cell data.

Gene coexpression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target inquiries at the cellular level. However, due to the sparsity and heterogeneity of transcript counts, it is challenging to construct accurate gene networks. We develop an approach, locCSN, that estimates cell-specific networks (CSNs) for each cell, preserving information about cellular heterogeneity that is lost with other approaches. LocCSN is based on a nonparametric investigation of the joint distribution of gene expression; hence it can readily detect nonlinear correlations, and it is more robust to distributional challenges. Although individual CSNs are estimated with considerable noise, average CSNs provide stable estimates of networks, which reveal gene communities better than traditional measures. Additionally, we propose downstream analysis methods using CSNs to utilize more fully the information contained within them. Repeated estimates of gene networks facilitate testing for differences in network structure between cell groups. Notably, with this approach, we can identify differential network genes, which typically do not differ in gene expression, but do differ in terms of the coexpression networks. These genes might help explain the etiology of disease. Finally, to further our understanding of autism spectrum disorder, we examine the evolution of gene networks in fetal brain cells and compare the CSNs of cells sampled from case and control subjects to reveal intriguing patterns in gene coexpression.

SCEPTRE improves calibration and sensitivity in single-cell CRISPR screen analysis.

Single-cell CRISPR screens are a promising biotechnology for mapping regulatory elements to target genes at genome-wide scale. However, technical factors like sequencing depth impact not only expression measurement but also perturbation detection, creating a confounding effect. We demonstrate on two single-cell CRISPR screens how these challenges cause calibration issues. We propose SCEPTRE: analysis of single-cell perturbation screens via conditional resampling, which infers associations between perturbations and expression by resampling the former according to a working model for perturbation detection probability in each cell. SCEPTRE demonstrates very good calibration and sensitivity on CRISPR screen data, yielding hundreds of new regulatory relationships supported by orthogonal biological evidence.

Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data.

Allelic expression imbalance (AEI), quantified by the relative expression of two alleles of a gene in a diploid organism, can help explain phenotypic variations among individuals. Traditional methods detect AEI using bulk RNA sequencing (RNA-seq) data, a data type that averages out cell-to-cell heterogeneity in gene expression across cell types. Since the patterns of AEI may vary across different cell types, it is desirable to study AEI in a cell-type-specific manner. Although this can be achieved by single-cell RNA sequencing (scRNA-seq), it requires full-length transcript to be sequenced in single cells of a large number of individuals, which are still cost prohibitive to generate. To overcome this limitation and utilize the vast amount of existing disease relevant bulk tissue RNA-seq data, we developed BSCET, which enables the characterization of cell-type-specific AEI in bulk RNA-seq data by integrating cell type composition information inferred from a small set of scRNA-seq samples, possibly obtained from an external dataset. By modeling covariate effect, BSCET can also detect genes whose cell-type-specific AEI are associated with clinical factors. Through extensive benchmark evaluations, we show that BSCET correctly detected genes with cell-type-specific AEI and differential AEI between healthy and diseased samples using bulk RNA-seq data. BSCET also uncovered cell-type-specific AEIs that were missed in bulk data analysis when the directions of AEI are opposite in different cell types. We further applied BSCET to two pancreatic islet bulk RNA-seq datasets, and detected genes showing cell-type-specific AEI that are related to the progression of type 2 diabetes. Since bulk RNA-seq data are easily accessible, BSCET provides a convenient tool to integrate information from scRNA-seq data to gain insight on AEI with cell type resolution. Results from such analysis will advance our understanding of cell type contributions in human diseases.

APOE and TREM2 regulate amyloid-responsive microglia in Alzheimer's disease.

Beta-amyloid deposition is a defining feature of Alzheimer's disease (AD). How genetic risk factors, like APOE and TREM2, intersect with cellular responses to beta-amyloid in human tissues is not fully understood. Using single-nucleus RNA sequencing of postmortem human brain with varied APOE and TREM2 genotypes and neuropathology, we identified distinct microglia subpopulations, including a subpopulation of CD163-positive amyloid-responsive microglia (ARM) that are depleted in cases with APOE and TREM2 risk variants. We validated our single-nucleus RNA sequencing findings in an expanded cohort of AD cases, demonstrating that APOE and TREM2 risk variants are associated with a significant reduction in CD163-positive amyloid-responsive microglia. Our results showcase the diverse microglial response in AD and underscore how genetic risk factors influence cellular responses to underlying pathologies.

Epilepsy in China: Factors influencing marriage status and fertility.

PURPOSE: To investigate the current status of marriage and fertility of patients with epilepsy (PWE) and characterize its influencing factors. METHODS: A total of 1,823 adult patients (males age 22 years or older, females age 20 years or older) were included in this study. Data concerning sociodemographic and clinical characteristics were collected. Descriptive analyses, followed by univariate and multivariate logistic regression analyses were utilized to examine factors associated with marriage and fertility of PWE. Marital status of PWE was compared with Chinese population. Standardized marriage rate (SMR) for age and sex was estimated based on the 2010 sixth national population census. RESULTS: 1,132 patients (62.1%) were married and 823 (45.1%) had a history of fertility. Patients had lower marriage rates than Chinese population (62.1% vs 78.4%). Patients with adult-onset epilepsy (>18 years) had a significantly higher rate of marriage and fertility (p ＜ 0.001) compared to those with childhood-onset epilepsy (≤18 years). Employed patients had higher marriage rates than unemployed patients (64.9% vs 58.6%, p = 0.006), with only male patients being significantly affected by employment status (p ＜ 0.001). Multiple logistic regression revealed that age, age at first seizure onset, and employment status were related to both marriage and fertility. CONCLUSION: Epilepsy had negative effects on marriage and fertility status. Marriage and fertility rates were lower in patients with Childhood-onset epilepsy (≤18 years). Furthermore, employment status mainly affected the marriage rate of male patients.

Bulk tissue cell type deconvolution with multi-subject single-cell expression reference.

Knowledge of cell type composition in disease relevant tissues is an important step towards the identification of cellular targets of disease. We present MuSiC, a method that utilizes cell-type specific gene expression from single-cell RNA sequencing (RNA-seq) data to characterize cell type compositions from bulk RNA-seq data in complex tissues. By appropriate weighting of genes showing cross-subject and cross-cell consistency, MuSiC enables the transfer of cell type-specific gene expression information from one dataset to another. When applied to pancreatic islet and whole kidney expression data in human, mouse, and rats, MuSiC outperformed existing methods, especially for tissues with closely related cell types. MuSiC enables the characterization of cellular heterogeneity of complex tissues for understanding of disease mechanisms. As bulk tissue data are more easily accessible than single-cell RNA-seq, MuSiC allows the utilization of the vast amounts of disease relevant bulk tissue RNA-seq data for elucidating cell type contributions in disease.

A novel naphthalene carboxylic acid-based ionic liquid mixed disperser combined with ultrasonic-enhanced in-situ metathesis reaction for preconcentration of triclosan and methyltriclosan in milk and eggs.

A microextraction method was developed based on utilization of a novel ionic liquid (IL) [C4MIM][NCA] as disperser and conventional ILs as extractor (IL-IL-DLLME). This method was integrated with an in-situ metathesis reaction to achieve high extraction efficiency by eliminating the loss of analytes in the discarded disperser after microextraction. Ultrasonic energy was compared to traditional mechanical shaking to accelerate the in-situ metathesis reaction. A 3-min ultrasonic treatment provided similar extraction efficiency as a 120-min mechanical shaking. Due to their strong acidity and lower solubility than traditional hydrophilic ILs, utilization of [C4MIM][NCA] in the IL-IL-DLLME procedure increased extraction recoveries (ERs) for triclosan (TCS) and methyltriclosan (MTCS) by 10-12% and also avoided an extra pH adjustment step. A series of operational parameters were optimized using single-factor screening and central composite design as follows: 65 µL extraction solvent, 150 µL [C4MIM][BF4] and [C4MIM][NCA] (132/18, v/v, µL) as dispersive solvent, 0.16 g NH4PF6 and 3.3 min ultrasonic time. Under optimized conditions with a fortification of 100 µg kg-1, ERs were 92.6-93.4% for TCS and 92.7-94.2% for MTCS in bovine milk and chicken egg samples. LODs for TCS and MTCS were 0.16-0.24 µg kg-1 and the enrichment factors were 21.8-23.1. Inter- and intra-day precisions had relative standard deviations of 3.3-5.4% for the optimized method. Overall, this newly developed IL-IL-DLLME method was effective for detecting trace levels of TCS and MTCS in real-world, animal-based foods. Prominent advantages of the new method include high precision and accuracy, high extraction efficiency, simple analytical operations, and no use of organic solvents making the procedure environmentally benign.

Extraction of triclosan and methyltriclosan in human fluids by in situ ionic liquid morphologic transformation.

Herein, we established an ionic liquid (IL)-based liquid-solid transformation microextraction (IL-LTME) combined with HPLC-UV detection for the simultaneous determination of triclosan (TCS) and its methylated product, methyltriclosan (MTCS), in human fluids. The IL-LTME method was based on an in situ metathesis between hydrophilic IL and ion-exchange salt to form a solid hydrophobic IL. According to the above principle, a hydrophilic IL, [C12MIM]Br, was selected as the extractant, and NH4PF6 as ion-exchange salt. The prominent advantages of the newly developed method are: (1) the in-situ reaction between the extractant [C12MIM]Br and ion-exchange salt NH4PF6 changed the IL from hydrophilic to hydrophobic that avoiding the stick of ionic liquid on the tube wall; (2) bubbling with NH3 greatly increased the contact area between IL-extractant and analytes resulting in improved extraction recovery; and (3) solidification of the [C12MIM] PF6 provided a good separation and avoided the use of specialized equipment. A series of main parameters were optimized by single-factor screening and central composite design as follows: 0.9 mL of NaOH, 2.0 min of second ultrasonically time, 10 min of centrifugation time, 21 mg of extractant [C12MIM]PF6, 2.4 min of ultrasonic time, 65 mg of NH4PF6 and 13.8 min of cooling time. Under the optimized conditions, the limits of detection for TCS and MTCS were 0.126-0.161 µg L-1 in plasma samples, and 0.211-0.254 µg L-1 in urine samples, respectively. The extraction recoveries for TCS and MTCS were in the range of 94.1-103.8%. The intra-day and inter-day precisions were 1.00-4.74% and 1.02-5.21%, respectively. In general, the IL-LTME method is environment-friendly, time-saving, economical, high efficient and robust with low detection limits and high recoveries. Thus, the newly developed method has excellent prospects for sample pretreatment and analysis of trace TCS and MTCS in blood and urine samples.

Sensitivity analysis and power for instrumental variable studies.

In observational studies to estimate treatment effects, unmeasured confounding is often a concern. The instrumental variable (IV) method can control for unmeasured confounding when there is a valid IV. To be a valid IV, a variable needs to be independent of unmeasured confounders and only affect the outcome through affecting the treatment. When applying the IV method, there is often concern that a putative IV is invalid to some degree. We present an approach to sensitivity analysis for the IV method which examines the sensitivity of inferences to violations of IV validity. Specifically, we consider sensitivity when the magnitude of association between the putative IV and the unmeasured confounders and the direct effect of the IV on the outcome are limited in magnitude by a sensitivity parameter. Our approach is based on extending the Anderson-Rubin test and is valid regardless of the strength of the instrument. A power formula for this sensitivity analysis is presented. We illustrate its usage via examples about Mendelian randomization studies and its implications via a comparison of using rare versus common genetic variants as instruments.