Pyroptosis inhibitors MCC950 and VX-765 mitigate myocardial injury by alleviating oxidative stress, inflammation, and apoptosis in acute myocardial hypoxia.

Acute myocardial infarction (AMI) is a prevalent cardiovascular disease with high morbidity and mortality rates worldwide. Pyroptosis is an inflammatory form of programmed cell death that has been linked to various pathological conditions. However, its exact contribution to the onset and progression of heart injury in AMI has not yet fully elucidated. Herein, we established mouse AMI model by ligating the left anterior descending artery and performed transcriptome analysis during the early phase of AMI. Mouse HL-1 and human AC-16 cardiomyocytes were subjected to hypoxia to simulate ischemic injury in vitro. Our results revealed a significant activation of the inflammatory response at 3 h post-ligation, as confirmed by RNA sequencing. We identified the occurrence of NLRP3 inflammasome-mediated pyroptosis in the cardiac tissues of human cases with AMI, as well as in mouse models of AMI and hypoxia-induced cardiomyocytes, using immunohistochemistry staining and Western blotting assays. Concurrently, pharmacological inhibition of NLRP3 inflammasome-mediated pyroptosis with MCC950 and VX-765 effectively decreased hypoxia-induced cardiomyocytes injury, while mitigating myocardial oxidative stress, apoptosis and inflammation caused by hypoxia. Moreover, the circulating levels of gasdermin D (GSDMD), the pyroptosis executor, were remarkably elevated in the plasma of mice with early AMI and in the supernatant of hypoxia-exposed cardiomyocytes in a time-dependent manner using ELISA and Western blotting. Furthermore, the change in circulating GSDMD positively correlated with Creatine Kinase-MB (CK-MB) in the plasma of early-stage AMI mouse. In summary, these findings indicated a critical role for NLRP3 inflammasome-mediated pyroptosis in the progression of AMI, the administration of MCC950 and VX-765 may be attractive candidate therapeutic approaches for cardiac injury caused by acute hypoxia or even AMI. Additionally, the circulating GSDMD exhibits potential as a newly diagnostic biomarker for AMI.

Tailoring Zirconia Supported Intermetallic Platinum Alloy via Reactive Metal-Support Interactions for High-Performing Fuel Cells.

Developing efficient and anti-corrosive oxygen reduction reaction (ORR) catalysts is of great importance for the applications of proton exchange membrane fuel cells (PEMFCs). Herein, we report a novel approach to prepare metal oxides supported intermetallic Pt alloy nanoparticles (NPs) via the reactive metal-support interaction (RMSI) as ORR catalysts, using Ni-doped cubic ZrO2 (Ni/ZrO2) supported L10-PtNi NPs as a proof of concept. Benefiting from the Ni migration during RMSI, the oxygen vacancy concentrations in the support are increased, leading to an electron enrichment of Pt. The optimal L10-PtNi-Ni/ZrO2-RMSI catalyst achieves remarkably low mass activity (MA) loss (17.8 %) after 400,000 accelerated durability test cycles in a half-cell and exceptional PEMFC performance (MA=0.76 A mgPt -1 at 0.9 V, peak power density=1.52/0.92 W cm-2 in H2-O2/-air, and 18.4 % MA decay after 30,000 cycles), representing the best reported Pt-based ORR catalysts without carbon supports. Density functional theory (DFT) calculations reveal that L10-PtNi-Ni/ZrO2-RMSI requires a lower energetic barrier for ORR than L10-PtNi-Ni/ZrO2 (direct loading), which is ascribed to a decreased Bader charge transfer between Pt and *OH, and the improved stability of L10-PtNi-Ni/ZrO2-RMSI compared to L10-PtNi-C can be contributed to the increased adhesion energy and Ni vacancy formation energy within the PtNi alloy.

Post-mortem genetic analysis of sudden unexplained death in a young cohort: a whole-exome sequencing study.

Sudden unexplained death (SUD) constitutes a considerable portion of unexpected sudden death in the young. Molecular autopsy has proved to be an efficient diagnostic tool in the multidisciplinary management of SUD. Yet, many cases remain undiagnosed using the widely adopted targeted genetic screening strategies. Here, we investigated the genetic substrates of a young SUD cohort (18-40 years old) from China using whole-exome sequencing (WES), with the primary aim to identify novel SUD susceptibility genes. Within 255 previously acknowledged SUD-associated genes, 21 variants with likely functional effects (pathogenic/likely pathogenic) were identified in 51.9% of the SUD cases. More importantly, a set of 33 candidate genes associated with myopathy were identified to be novel susceptibility genes for SUD. Comparative analysis of the cumulative PHRED-scaled CADD score and polygenetic burden score showed that the amount and deleteriousness of variants in the 255 SUD-associated genes and the 33 candidate genes identified by this study were significantly higher compared with 289 randomly selected genes. A significantly higher genetic burden of rare variants (MAF < 0.1%) in the 33 candidate genes also highlighted putative roles of these genes in SUD. After incorporating these novel genes, the genetic testing yields of the current SUD cohort elevated from 51.9 to 66.7%. Our study expands understanding of the genetic variants underlying SUD and presents insights that improve the utility of genetic screenings.

Exposure of Salmonella enterica serovar 1,4,[5],12:i:- to benzalkonium chloride leads to acquired resistance to this disinfectant and antibiotics.

AIMS: Disinfectants such as benzalkonium chloride (BC), extensively used in animal farms and food-processing industries, contribute to the development of adaptive and cross-resistance in foodborne pathogens, posing a serious threat to food safety and human health. The purpose of this study is to explore whether continuous exposure of Salmonella enterica serovar 1,4,[5],12:i:- (S. 1,4,[5],12:i:-) to sublethal concentrations of BC could result in acquired resistance to this agent and other environmental stresses (e.g. antibiotics, heat, and acid). METHODS AND RESULTS: BC tolerance increased in all tested strains after exposure to gradually increasing concentrations of BC, with increases in minimum inhibitory concentrations between two and sixfold. The survival rate of BC-adapted strains was significantly (P < 0.05) higher than that of their wild-type (non-adapted) counterparts in lethal concentrations of BC. In addition, significant reductions (P < 0.05) in zeta potential were observed in BC-adapted strains compared to wild-type ones, indicating that a reduction in cell surface charge was a cause of adaptative resistance. More importantly, two BC-adapted strains exhibited increased antibiotic resistance to levofloxacin, ceftazidime, and tigecycline, while gene mutations (gyrA, parC) and antibiotic efflux-related genes (acrB, mdsA, mdsB) were detected by genomic sequencing analysis. Moreover, the tolerance of BC-adapted strains to heat (50, 55, and 60°C) and acid (pH 2.0, 2.5) was strain-dependent and condition-dependent. CONCLUSIONS: Repeated exposure to sublethal concentrations of BC could result in the emergence of BC- and antibiotic-resistant S. 1,4,[5],12:i:- strains.

Distribution-based maximum likelihood estimation methods are preferred for estimating Salmonella concentration in chicken when contamination data are highly left-censored.

Salmonella is a common chicken-borne pathogen that causes human infections. Data below the detection limit, referred to as left-censored data, are frequently encountered in the detection of pathogens. The approach of handling the censored data was regarded to affect the estimation accuracy of microbial concentration. In this study, a set of Salmonella contamination data was collected from chilled chicken samples using the most probable number (MPN) method, which consisted of 90.42% (217/240) non-detect values. Two simulated datasets with fixed censoring degrees of 73.60% and 90.00% were generated based on the real-sampling Salmonella dataset for comparison. Three methodologies were applied for handling left-censored data: (i) substitution with different alternatives, (ii) the distribution-based maximum likelihood estimation (MLE) method, and (iii) the multiple imputation (MI) method. For each dataset, the negative binomial (NB) distribution-based MLE and zero-modified NB distribution-based MLE were preferable for highly censored data and resulted in the least root mean square error (RMSE). Replacing the censored data with half the limit of quantification was the next best method. The mean concentration of Salmonella monitoring data estimated by the NB-MLE and zero-modified NB-MLE methods was 0.68 MPN/g. This study provided an available statistical method for handling bacterial highly left-censored data.

A stable solid-state lithium-oxygen battery enabled by heterogeneous silicon-based interface.

Solid-state lithium-metal batteries using inorganic solid-state electrolyte (SSE) instead of liquid-electrolyte, especially lithium-oxygen (Li-O2) battery, have attracted much more attention due to their high-energy density and safety. However, the poor interface contact between electrodes and SSEs makes these batteries lose most of their capacity and power during cycling. Here we report that by coating a heterogeneous silicon carbide on lithium metal anode and Li1.5Al0.5Ge1.5P3O12(LAGP)-SSE, a good interface contact is created between the electrode and electrolyte that can effectively reduce the interface impedance and improve the cycle performance of the assembled battery. As a result, the solid-sate Li-O2battery demonstrates a cycle lifespan of ∼78 cycles being at least 3-times higher than the solid-state Li-O2battery without silicon carbide with a capacity limitation of 1000 mAh g-1at 250 mA g-1. The characterization of discharge products indicates a typical two-electron convention of oxygen-to-lithium oxide for the solid-state Li-O2battery system. This work paves a way for developing high-energy long-cycle solid-state lithium-metal battery. The work provides insights into the interface between the Li-metal and SSE to develop high-energy long-cycle all solid-state Li-metal batteries.

Ethyl sulfate in blood shows the potential to distinguish alcoholic death and postmortem alcohol instillation.

Alcohol is often found in the blood of the deceased. To cover up the true cause of victim's death, postmortem instillation of alcohol occurs in some criminal cases. Explaining the finding of alcohol is extremely vital in forensic practice. This study aims to evaluate whether ethyl glucuronide (EtG) and ethyl sulfate (EtS) in blood and vitreous humor (VH) can be used to distinguish alcoholic death and postmortem alcohol instillation. Saline or 12.6 g/kg ethanol (antemortem alcohol poisoning group) was introduced into rabbits' stomachs 2 h before sacrificed. Same amount of ethanol was introduced into rabbits' stomachs at 0 h, 0.5 h, 1 h and 2 h after death in four subgroups of postmortem alcohol instillation group, respectively. Cardiac blood and VH were collected at 10 min, 4 h, 10 h and 24 h after death in blank and antemortem alcohol poisoning group, and after instillation of alcohol in postmortem alcohol instillation group. Blood was also collected at 34 h. Ethanol and EtG levels in blood and VH and EtS in VH in antemortem alcohol poisoning group were overlapped with those in postmortem alcohol instillation group. The contents of EtG and EtS in blood in antemortem alcohol poisoning group (mean ≥ 7.833 µg/mL for EtG and ≥ 19.990 µg/mL for EtS) were much higher than those in postmortem alcohol instillation group (mean ≤ 0.118 µg/mL for EtG and ≤ 0.091 µg/mL for EtS), but apparent decomposition was observed in EtG, which might lead to misinterpretation. Blood EtS showed better stability and could be used to distinguish alcoholic death and postmortem alcohol instillation.

Serum SELENBP1 and VCL Are Effective Biomarkers for Clinical and Forensic Diagnosis of Coronary Artery Spasm.

Coronary artery spasm (CAS) plays an important role in the pathogenesis of many ischemic heart entities; however, there are no established diagnostic biomarkers for CAS in clinical and forensic settings. This present study aimed to identify such serum biomarkers by establishing a rabbit CAS provocation model and integrating quantitative serum proteomics, parallel reaction monitoring/mass spectrometry-based targeted proteomics, and partial least-squares discriminant analysis (PLS-DA). Our results suggested that SELENBP1 and VCL were potential candidate biomarkers for CAS. In independent clinical samples, SELENBP1 and VCL were validated to be significantly lower in serum but not blood cells from CAS patients, with the reasons for this possibly due to the decreased secretion from cardiomyocytes. The areas under the curve of the receiver operating characteristics (ROC) analysis were 0.9384 for SELENBP1 and 0.9180 for VCL when diagnosing CAS. The CAS risk decreased by 32.3% and 53.6% for every 10 unit increases in the serum SELENBP1 and VCL, respectively. In forensic samples, serum SELENBP1 alone diagnosed CAS-induced deaths at a sensitivity of 100.0% and specificity of 72.73%, and its combination with VCL yielded a diagnostic specificity of 100.0%, which was superior to the traditional biomarkers of cTnI and CK-MB. Therefore, serum SELENBP1 and VCL could be effective biomarkers for both the clinical and forensic diagnosis of CAS.

Machine learning approach for predicting single cell lag time of Salmonella Enteritidis after heat and chlorine treatment.

The importance of single-cell variability is increasingly prominent with the developments in foodborne pathogens modeling. Traditional predictive microbiology model cannot accurately describe the growth behavior of small numbers of cells due to individual cell heterogeneity. The objective of the present study was to develop predictive models for single cell lag times of Salmonella Enteritidis after heat and chlorine treatment. A time-lapse microscopy method was employed to evaluate the single cell lag time by monitoring cell divisions. Four supervised machine learning algorithms including gradient boosting regression tree (GBRT), artificial neural network (ANN), random forest (RF), and support vector regression (SVR) were applied and compared. Results show that all four machine learning models have good predictive capabilities without an overfitting of the data. The ANN approach demonstrated superior prediction performance over other machine learning models (RMSE: 0.209, MAE: 0.135 and R2: 0.989). Furthermore, the SHapley Additive exPlanation (SHAP) measures were used to capture the influence of each feature on the model output, and results revealed that population lag times and sublethal injury rate have dominant impacts on the single cell lag time. Consequently, the findings generated from this study may be useful in managing the potential food safety risk caused by single cells of foodborne pathogens.

Non/mini-invasive monitoring of diabetes-induced myocardial damage by Fourier transform infrared spectroscopy: Evidence from biofluids.

Early identification of diabetic cardiomyopathy (DCM) can help clinicians develop targeted treatment plans and forensic pathologists make accurate postmortem diagnoses. In the present study, diabetes-induced metabolic abnormalities in the myocardium and biofluids (plasma, urine, and saliva) of db/db mice of various ages (7, 12, and 21 weeks) were investigated by attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy. The results indicated that the diabetic and control groups had significantly different changes in the function groups of lipids, phosphate macromolecules (mostly nucleic acids), protein compositions and conformations, and carbohydrates (primarily glucose) in the myocardium and biofluids. The prediction model for quantifying DCM severity was developed on db/db mice's myocardial spectra using a genetic algorithm (GA)-partial least squares (PLS) regression method. Following that, the linear correlations between the predicted values for DCM severity and spectra for db/db biofluids were evaluated using the GA-PLS regression algorithm. The results showed there were good linear correlations between the predicted values for DCM severity and spectra for plasma (R2 = 0.929), saliva (R2 = 0.967), urine (R2 = 0.954), and combination of plasma and saliva (R2 = 0.980). This study provides a novel perspective on detecting diabetes-related biofluid and cardiac metabolic abnormalities and demonstrates the potential of biofluid infrared spectro-diagnostic models for non/mini-invasive assessment of DCM.

Investigation of early biochemical alterations in myocardia of the diabetic db/db mice by FTIR microspectroscopy combined with machine learning.

Diabetic cardiomyopathy (DbCM) is a serious complication of diabetes that affects about 12% of the diabetic population. Sensitive detection of diabetes-induced biochemical changes in the heart before symptoms appear can assist clinicians in developing targeted treatment plans and forensic pathologists in making accurate postmortem diagnoses. The Fourier transform infrared (FTIR) spectroscopy-based approach allows for the analysis of the sample biomolecular composition and variations. In the current study, the myocardial tissues of mouse models of type 2 diabetes mellitus (T2DM) at various ages (7, 12, and 21 weeks) were analyzed using FTIR microspectroscopy (FTIRM) in combination with machine learning algorithms. The carbonyl esters, olefinic=CH and CH2 groups of lipids, total lipids, saccharides, and ß-sheet to α-helix conformational transition in proteins increased significantly in diabetic mice myocardial tissues compared to healthy mice. Furthermore, partial least-squares discriminant analysis and random forest-guided partial least-squares discriminant analysis revealed the time-dependent progression of the spectral lipidomic profiles during the development of DbCM. Finally, a random forest classifier was developed for diagnosing DbCM, with 97.1% accuracy. This study demonstrates that FTIRM is a novel method for monitoring early biochemical changes in the myocardia of mice with T2DM.

Three-Dimensional Large-Scale Fused Silica Microfluidic Chips Enabled by Hybrid Laser Microfabrication for Continuous-Flow UV Photochemical Synthesis.

We demonstrate a hybrid laser microfabrication approach, which combines the technical merits of ultrafast laser-assisted chemical etching and carbon dioxide laser-induced in situ melting for centimeter-scale and bonding-free fabrication of 3D complex hollow microstructures in fused silica glass. With the developed approach, large-scale fused silica microfluidic chips with integrated 3D cascaded micromixing units can be reliably manufactured. High-performance on-chip mixing and continuous-flow photochemical synthesis under UV irradiation at ~280 nm were demonstrated using the manufactured chip, indicating a powerful capability for versatile fabrication of highly transparent all-glass microfluidic reactors for on-chip photochemical synthesis.

High-Throughput Continuous-Flow Separation in a Micro Free-Flow Electrophoresis Glass Chip Based on Laser Microfabrication.

Micro free-flow electrophoresis (µFFE) provides a rapid and straightforward route for the high-performance online separation and purification of targeted liquid samples in a mild manner. However, the facile fabrication of a µFFE device with high throughput and high stability remains a challenge due to the technical barriers of electrode integration and structural design for the removal of bubbles for conventional methods. To address this, the design and fabrication of a high-throughput µFFE chip are proposed using laser-assisted chemical etching of glass followed by electrode integration and subsequent low-temperature bonding. The careful design of the height ratio of the separation chamber and electrode channels combined with a high flow rate of buffer solution allows the efficient removal of electrolysis-generated bubbles along the deep electrode channels during continuous-flow separation. The introduction of microchannel arrays further enhances the stability of on-chip high-throughput separation. As a proof-of-concept, high-performance purification of fluorescein sodium solution with a separation purity of ~97.9% at a voltage of 250 V from the mixture sample solution of fluorescein sodium and rhodamine 6G solution is demonstrated.

Stroke Risk Prediction With Hybrid Deep Transfer Learning Framework.

Stroke has become a leading cause of death and long-term disability in the world with no effective treatment. Deep learning-based approaches have the potential to outperform existing stroke risk prediction models, but they rely on large well-labeled data. Due to the strict privacy protection policy in health-care systems, stroke data is usually distributed among different hospitals in small pieces. In addition, the positive and negative instances of such data are extremely imbalanced. Transfer learning can solve small data issue by exploiting the knowledge of a correlated domain, especially when multiple source of data are available. In this work, we propose a novel Hybrid Deep Transfer Learning-based Stroke Risk Prediction (HDTL-SRP) scheme to exploit the knowledge structure from multiple correlated sources (i.e., external stroke data, chronic diseases data, such as hypertension and diabetes). The proposed framework has been extensively tested in synthetic and real-world scenarios, and it outperforms the state-of-the-art stroke risk prediction models. It also shows the potential of real-world deployment among multiple hospitals aided with 5 G/B5G infrastructures.

Imbalance of Autophagy and Apoptosis Induced by Oxidative Stress May Be Involved in Thyroid Damage Caused by Sleep Deprivation in Rats.

Many studies have shown that sleep deprivation can affect a wide range of tissues and organs, and most of these effects are related to oxidative stress. Oxidative stress can cause functional and morphological changes in cells, which are closely related to autophagy and apoptosis. In this study, we examined changes in thyroid morphology and function, oxidative stress, autophagy, and apoptosis in rats after sleep deprivation. Thyroid hormones, thyroid-stimulating hormone, functional substances required for the synthesis of thyroid hormones, and thyroid morphological observations were used to evaluate the changes and impairment of thyroid function. Methane dicarboxylic aldehyde and total antioxidant capacity were measured to assess oxidative stress in the thyroid. To evaluate the balance of autophagy and apoptosis, the expression of autophagy- and apoptosis-related proteins was examined by western blotting, and apoptotic cells were labeled with TUNEL staining. The body weight of rats in the sleep deprivation group decreased, but the relative weight of the thyroid gland increased. Sleep deprivation led to morphological changes in the thyroid. The levels of thyroid hormones and thyroid-stimulating hormone increased after sleep deprivation. Total antioxidant capacity decreased, and methane dicarboxylic aldehyde levels increased in the thyroid in the sleep deprivation group. Analysis of autophagy- and apoptosis-related proteins indicated that the microtubule-associated protein 1 light chain 3 beta- (LC3B-) and LC3A-II/I ratio and Beclin 1 levels significantly decreased in the sleep deprivation group and P62 levels significantly increased. The number of apoptotic cells in the thyroid gland of sleep-deprived rats increased significantly. Taken together, these results indicate that sleep deprivation can lead to oxidative stress in the thyroid and ultimately cause thyroid damage, which may be related to the imbalance of autophagy and apoptosis.

Genomic selection for heterobothriosis resistance concurrent with body size in the tiger pufferfish, Takifugu rubripes.

Parasite resistance traits in aquaculture species often have moderate heritability, indicating the potential for genetic improvements by selective breeding. However, parasite resistance is often synonymous with an undesirable negative correlation with body size. In this study, we first tested the feasibility of genomic selection (GS) on resistance to heterobothriosis, caused by the monogenean parasite Heterobothrium okamotoi, which leads to huge economic losses in aquaculture of the tiger pufferfish Takifugu rubripes. Then, using a simulation study, we tested the possibility of simultaneous improvement of parasite resistance, assessed by parasite counts on host fish (HC), and standard length (SL). Each trait showed moderate heritability (square-root transformed HC: h2 = 0.308 ± 0.123, S.E.; SL: h2 = 0.405 ± 0.131). The predictive abilities of genomic prediction among 12 models, including genomic Best Linear Unbiased Predictor (GBLUP), Bayesian regressions, and machine learning procedures, were also moderate for both transformed HC (0.248‒0.344) and SL (0.340‒0.481). These results confirmed the feasibility of GS for this trait. Although an undesirable genetic correlation was suggested between transformed HC and SL (rg = 0.228), the simulation study suggested the desired gains index can help achieve simultaneous genetic improvements in both traits.

Evaluation and review of ways to differentiate sources of ethanol in postmortem blood.

Accurate determination of a person's blood alcohol concentration (BAC) is an important task in forensic toxicology laboratories because of the existence of statutory limits for driving a motor vehicle and workplace alcohol testing regulations. However, making a correct interpretation of the BAC determined in postmortem (PM) specimens is complicated, owing to the possibility that ethanol was produced in the body after death by the action of various micro-organisms (e.g., Candida species) and fermentation processes. This article reviews various ways to establish the source of ethanol in PM blood, including collection and analysis of alternative specimens (e.g., bile, vitreous humor (VH), and bladder urine), the identification of non-oxidative metabolites of ethanol, ethyl glucuronide (EtG) and ethyl sulfate (EtS), the urinary metabolites of serotonin (5-HTOL/5-HIAA), and identification of n-propanol and n-butanol in blood, which are known putrefaction products. Practical utility of the various biomarkers including specificity and stability is discussed.

Understanding the functions of endogenous DOF transcript factor in Chlamydomonas reinhardtii.

BACKGROUND: The regulation of genes related to lipid metabolism by genetic engineering is an important way to increase the accumulation of lipids in microalgae. DNA binding with one finger (DOF) is a plant-specific transcription factor in higher plants, where it regulates carbon and nitrogen metabolic pathways by regulating key genes involved in these pathways. Overexpression of DOF can increase lipid production in plants; however, it is not clear whether overexpression of DOF can increase lipids in microalgae. RESULTS: In this study, we cloned a DOF transcription factor, crDOF, from Chlamydomonas reinhardtii. The sequence of this transcription factor is 1875 bp and encodes a peptide of 624 amino acids with a conserved DOF domain. Overexpression of crDOF in C. reinhardtii significantly increased the intracellular lipid content. The content of total fatty acids in the transgenic algae line Tranc-crDOF-12 was 126.01 µg/mg (dry weight), which was 23.24% higher than that of the wild type. Additionally, the content of unsaturated fatty acids in the transgenic Tranc-crDOF-12 line increased significantly. Fluorescence quantitative PCR analysis showed that in the transgenic line Tranc-crDOF-12, the expression levels of BCC1, FAT1, SQD1, MGD1, DGD1 and PGP1 genes were significantly upregulated, while the expression levels of ACP1, ACS1, CIS1 and SQD2 were downregulated. CONCLUSIONS: Our results confirm that crDOF increases intracellular lipids in C. reinhardtii by regulating key genes involved in lipid metabolism. According to these findings, we propose that enhancing the lipid content in microalgae by overexpressing DOF may be achieved in other industrial strains of microalgae and be employed for the industrial production of biodiesel.

Common genetic variants have associations with human cortical brain regions and risk of schizophrenia.

Schizophrenia is a highly heritable mental disorder and is reported to be associated with measurements in cortical regions of the human brain. In this study, we considered genome-wide association studies to uncover genetic effects on cortical regions and prodromal symptoms of schizophrenia. Specifically, area, thickness, and volume of 66 cortical regions derived from magnetic resonance imaging scans of 1,445 children and adolescents from the Philadelphia Neurodevelopmental Cohort were studied. Two common variants were identified as being associated with two prefrontal cortical regions (one significant variant rs11601331 on chromosome 11p11 for right rostral middle frontal gyral area, p = 1.97 × 10 -8 ; one suggestive variant rs2345981 on chromosome 6q11 for left frontal pole gyral volume, p = 2.07 × 10 -7 ), where the significance of rs11601331 was independently replicated on the Pediatric Imaging, Neurocognition, and Genetics study of size 1,239 (p = 9.19 × 10 -3 ). Moreover, genetic effects on schizophrenia were investigated based on a sample of 8,719 subjects. The two identified variants rs11601331 and rs2345981 showed significant association with the longest prodromal symptoms duration (p = 0.048 and p = 0.027, respectively).