Electronegative Phosphorus-Integrated Co2+ Active Sites for Enhanced Electrocatalytic Nitrogen Reduction.

In the quest for proficient electrocatalysts for ammonia's electrocatalytic nitrogen reduction, cobalt oxides, endowed with a rich d-electron reservoir, have emerged as frontrunners. Despite the previously evidenced prowess of CoO in this realm, its ammonia yield witnesses a pronounced decline as the reaction unfolds, a phenomenon linked to the electron attrition from its Co2+ active sites during electrocatalytic nitrogen reduction reaction (ENRR). To counteract this vulnerability, we harnessed electron-laden phosphorus (P) elements as dopants, aiming to recalibrate the electronic equilibrium of the pivotal Co active site, thereby bolstering both its catalytic performance and stability. Our empirical endeavors showcased the doped P-CoO's superior credentials: it delivered an impressive ammonia yield of 49.6 and, notably, a Faradaic efficiency (FE) of 9.6% at -0.2 V versus RHE, markedly eclipsing its undoped counterpart. Probing deeper, a suite of ex-situ techniques, complemented by rigorous theoretical evaluations, was deployed. This dual-pronged analysis unequivocally revealed CoO's propensity for an electron-driven valence metamorphosis to Co3+ post-ENRR. In stark contrast, P-CoO, fortified by P doping, exhibits a discernibly augmented ammonia yield. Crucially, P's intrinsic ability to staunch electron leakage from the active locus during ENRR ensures the preservation of the valence state, culminating in enhanced catalytic dynamism and fortitude. This investigation not only illuminates the intricacies of active site electronic modulation in ENRR but also charts a navigational beacon for further enhancements in this domain.

Zn Single-Atom Catalysts Enable the Catalytic Transfer Hydrogenation of α,ß-Unsaturated Aldehydes.

Highly active nonprecious-metal single-atom catalysts (SACs) toward catalytic transfer hydrogenation (CTH) of α,ß-unsaturated aldehydes are of great significance but still are deficient. Herein, we report that Zn-N-C SACs containing Zn-N3 moieties can catalyze the conversion of cinnamaldehyde to cinnamyl alcohol with a conversion of 95.5% and selectivity of 95.4% under a mild temperature and atmospheric pressure, which is the first case of Zn-species-based heterogeneous catalysts for the CTH reaction. Isotopic labeling, in situ FT-IR spectroscopy, and DFT calculations indicate that reactants, coabsorbed at the Zn sites, proceed CTH via a "Meerwein-Ponndorf-Verley" mechanism. DFT calculations also reveal that the high activity over Zn-N3 moieties stems from the suitable adsorption energy and favorable reaction energy of the rate-determining step at the Zn active sites. Our findings demonstrate that Zn-N-C SACs hold extraordinary activity toward CTH reactions and thus provide a promising approach to explore the advanced SACs for high-value-added chemicals.

Functional differentiation of olive PLP_deC genes: insights into metabolite biosynthesis and genetic improvement at the whole-genome level.

KEY MESSAGE: This study identified 16 pyridoxal phosphate-dependent decarboxylases in olive at the whole-genome level, conducted analyses on their physicochemical properties, evolutionary relationships and characterized their activity. Group II pyridoxal phosphate-dependent decarboxylases (PLP_deC II) mediate the biosynthesis of characteristic olive metabolites, such as oleuropein and hydroxytyrosol. However, there have been no report on the functional differentiation of this gene family at the whole-genome level. This study conducted an exploration of the family members of PLP_deC II at the whole-genome level, identified 16 PLP_deC II genes, and analyzed their gene structure, physicochemical properties, cis-acting elements, phylogenetic evolution, and gene expression patterns. Prokaryotic expression and enzyme activity assays revealed that OeAAD2 and OeAAD4 could catalyze the decarboxylation reaction of tyrosine and dopa, resulting in the formation of their respective amine compounds, but it did not catalyze phenylalanine and tryptophan. Which is an important step in the synthetic pathway of hydroxytyrosol and oleuropein. This finding established the foundational data at the molecular level for studying the functional aspects of the olive PLP_deC II gene family and provided essential gene information for genetic improvement of olive.

Copy number variation sequencing for the products of conception: What is the optimal testing strategy.

BACKGROUND: Copy number variation sequencing (CNV-seq) is crucial in prenatal diagnosis, but its limitations in detecting polyploidy, maternal cell contamination (MCC), and uniparental disomy (UPD) restrict its application in the analysis of products of conception (POCs). This study aimed to investigate an optimal genetic testing strategy for POCs in the era of CNV-seq. METHODS: CNV-seq and quantitative fluorescent polymerase chain reaction (QF-PCR) were performed in all 4,211 spontaneous miscarriage cases. Different testing strategies were compared and the optimal testing strategies were proposed. RESULTS: Of the 4,211 cases, 2561 (60.82%) exhibited clinically significant chromosomal abnormalities. CNV-seq alone, without QF-PCR, might misdiagnose 311 (7.39%) cases, including 278 polyploidy, 13 UPD, and 20 MCC. In 20 MCC cases identified by QF-PCR, CNV-seq successfully pinpointed the cause of miscarriage in 13 cases. Furthermore, in cases where QF-PCR suggested polyploidy, CNV-seq improved the diagnostic accuracy in 54 (1.28%) hypo/hypertriploidy cases. After comparing four different strategies, the sequential approach (initiating with CNV-seq followed by QF-PCR if necessary) emerged as advantageous, reducing approximately 70% of the cost associated with QF-PCR while maintaining result accuracy. CONCLUSIONS: We propose an initial CNV-seq followed by QF-PCR if needed-an efficient and cost-effective strategy for the genetic analysis of POCs.

Light Absorption Analysis and Optimization of Ag@TiO2 Core-Shell Nanospheroid and Nanorod.

For photothermal therapy of cancer, it is necessary to find Ag @TiO2 core-shell nanoparticles that can freely tune the resonance wavelength within the near-infrared biological window. In this paper, the finite element method and the size-dependent refractive index of metal nanoparticles were used to theoretically investigate the effects of the core material, core length, core aspect ratio, shell thickness, refractive index of the surrounding medium, and the particle orientation on the light absorption properties of Ag@TiO2 core-shell nanospheroid and nanorod. The calculations show that the position and intensity of the light absorption resonance peaks can be freely tuned within the first and second biological windows by changing the above-mentioned parameters. Two laser wavelengths commonly used in photothermal therapy, 808 nm (first biological window) and 1064 nm (second biological window), were selected to optimize the core length and aspect ratio of Ag@TiO2 core-shell nanospheroid and nanorod. It was found that the optimized Ag@TiO2 core-shell nanospheroid has a stronger light absorption capacity at the laser wavelengths of 808 nm and 1064 nm. The optimized Ag@TiO2 core-shell nanoparticles can be used as ideal therapeutic agents in photothermal therapy.

Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction.

Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein-ligand interaction prediction has demonstrated significant potential. In this paper, computational methods, focusing on sequence and structure to study protein-ligand interactions, are examined. Therefore, this paper starts by presenting an overview of the data sets applied in this area, as well as the various approaches applied for representing proteins and ligands. Then, sequence-based and structure-based classification criteria are subsequently utilized to categorize and summarize both the classical machine learning models and deep learning models employed in protein-ligand interaction studies. Moreover, the evaluation methods and interpretability of these models are proposed. Furthermore, delving into the diverse applications of protein-ligand interaction models in drug research is presented. Lastly, the current challenges and future directions in this field are addressed.

The DFT and Machine Learning Method Accelerated the Discovery of DMSCs with High ORR and OER Catalytic Activities.

The oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER) are crucial for the conversion of clean energy. Recently, dual-metal-site catalysts (DMSCs) have gained much attention due to their high atom utilization, stronger stability, and better catalytic performance. An advanced method that combines density functional theory (DFT) and machine learning (ML) has been employed in this study to investigate the adsorption free energies of adsorbates on hundreds of potential catalysts, with the aim of screening for catalysts that are highly active for the ORR and OER. The result of this study is that 30 DMSCs with ORR activity superior to Pt, 10 DMSCs with OER activity superior to RuO2, and 4 bifunctional catalysts for the OER and ORR are identified. This work provides guidance for the rational selection of metals on DMSCs to prepare catalysts with a high electrocatalytic performance for renewable energy applications.

Energizing Co Active Sites via d-Band Center Engineering in CeO2 -Co3 O4 Heterostructures: Interfacial Charge Transfer Enabling Efficient Nitrate Electrosynthesis.

The electrochemical nitrogen oxidation reaction (NOR) holds significant potential to revolutionize the traditional nitrate synthesis processes. However, the progression in NOR has been notably stymied due to the sluggish kinetics of initial N2 adsorption and activation processes. Herein, the research embarks on the development of a CeO2 -Co3 O4 heterostructure, strategically engineered to facilitate the electron transfer from CeO2 to Co3 O4 . This orchestrated transfer operates to amplify the d-band center of the Co active sites, thereby enhancing N2 adsorption and activation dynamics by strengthening the Co─N bond and diminishing the resilience of the N≡N bond. The synthesized CeO2 -Co3 O4 manifests promising prospects, showcasing a significant HNO3 yield of 37.96 µg h-1 mgcat -1 and an elevated Faradaic efficiency (FE) of 29.30% in a 0.1 m Na2 SO4 solution at 1.81 V versus RHE. Further substantiating these findings, an array of in situ methodologies coupled with DFT calculations vividly illustrate the augmented adsorption and activation of N2 on the surface of CeO2 -Co3 O4 heterostructure, resulting in a substantial reduction in the energy barrier pertinent to the rate-determining step within the NOR pathway. This research carves a promising pathway to amplify N2 adsorption throughout the electrochemical NOR operations and delineates a blueprint for crafting highly efficient NOR electrocatalysts.

Biallelic variants in RBM42 cause a multisystem disorder with neurological, facial, cardiac, and musculoskeletal involvement.

Here, we report a previously unrecognized syndromic neurodevelopmental disorder associated with biallelic loss-of-function variants in the RBM42 gene. The patient is a 2-year-old female with severe central nervous system (CNS) abnormalities, hypotonia, hearing loss, congenital heart defects, and dysmorphic facial features. Familial whole-exome sequencing (WES) reveals that the patient has two compound heterozygous variants, c.304C>T (p.R102*) and c.1312G>A (p.A438T), in the RBM42 gene which encodes an integral component of splicing complex in the RNA-binding motif protein family. The p.A438T variant is in the RRM domain which impairs RBM42 protein stability in vivo. Additionally, p.A438T disrupts the interaction of RBM42 with hnRNP K, which is the causative gene for Au-Kline syndrome with overlapping disease characteristics seen in the index patient. The human R102* or A438T mutant protein failed to fully rescue the growth defects of RBM42 ortholog knockout ΔFgRbp1 in Fusarium while it was rescued by the wild-type (WT) human RBM42. A mouse model carrying Rbm42 compound heterozygous variants, c.280C>T (p.Q94*) and c.1306_1308delinsACA (p.A436T), demonstrated gross fetal developmental defects and most of the double mutant animals died by E13.5. RNA-seq data confirmed that Rbm42 was involved in neurological and myocardial functions with an essential role in alternative splicing (AS). Overall, we present clinical, genetic, and functional data to demonstrate that defects in RBM42 constitute the underlying etiology of a new neurodevelopmental disease which links the dysregulation of global AS to abnormal embryonic development.

Managing the Severe Cavus Foot Deformity in Global Humanitarian Programs.

The cavus foot represents a complex spectrum of deformity ranging from the subtle idiopathic to the severe sensorimotor neuropathy and other neuromuscular deformities. The successful surgical treatment of the cavus foot depends on a fundamental understanding of the underlying multiplanar deformity, inherent muscle balance, and the rigidity of the hindfoot. The location of the deformity is described and understood according to its multiple apices. These deformities are addressed with osteotomies or arthrodesis directed at the apices of deformity. Simultaneously, correction of muscular imbalances with appropriate tendon transfers must also be performed to prevent recurrent deformity. With these principles in mind, the surgical correction of the cavus foot becomes simplified and algorithmically driven.

SNHG1/miR-21 axis mediates the cardioprotective role of aloin in sepsis through modulating cardiac cell viability and inflammatory responses.

BACKGROUND: Aloin has cardioprotective effects, however, its cardioprotective role in sepsis remains unclear. This study aimed to analyze whether aloin could prevent sepsis-related myocardial damage and explore the underlying mechanisms by examining the expression of long-noncoding RNA (lncRNA) SNHG1 and microRNA-21 (miR-21). METHODS: The interaction of SNHG1 with miR-21 was identified by dual-luciferase reporter assay. The levels of SNHG1 and miR-21 were measured by real-time quantitative PCR. The cardioprotective function of aloin was assessed in a sepsis animal model, which was induced by cecal ligation and puncture, and in a myocardial injury cell model in H9C2 cells stimulated by lipopolysaccharide. Myocardial injury biomarker levels and hemodynamic indicators in mice model were measured to evaluate cardiac function. The viability of H9C2 cells was assessed by cell counting kit-8 assay. Inflammatory cytokine levels were examined by an ELISA method. RESULTS: Decreased SNHG1 and increased miR-21 were found in sepsis patients with cardiac dysfunction, and they were negatively correlated. Aloin significantly attenuated myocardial damage and inflammatory responses of mice model, and increased the viability and suppressed inflammation in H9C2 cell model. In addition, SNHG1 expression was upregulated and miR-21 expression was downregulated by aloin in both mice and cell models. Moreover, in mice and cell models, SNHG1/miR-21 axis affected sepsis-related myocardial damage, and mediated the cardioprotective effects of aloin. CONCLUSION: Our findings indicated that aloin exerts protective effects in sepsis-related myocardial damage through regulating cardiac cell viability and inflammatory responses via regulating the SNHG1/miR-21 axis.

An Effective and Universal Long-Read Sequencing-Based Approach for SMN1 2 + 0 Carrier Screening through Family Trio Analysis.

BACKGROUND: Population-wide carrier screening for spinal muscular atrophy (SMA) is recommended by professional organizations to facilitate informed reproductive options. However, genetic screening for SMN1 2 + 0 carriers, accounting for 3%-8% of all SMA carriers, has been challenging due to the large gene size and long distance between the 2 SMN genes. METHODS: Here we repurposed a previously developed long-read sequencing-based approach, termed comprehensive analysis of SMA (CASMA), to identify SMN1 2 + 0 carriers through haplotype analysis in family trios (CASMA-trio). Bioinformatics pipelines were developed for accurate haplotype analysis and SMN1 2 + 0 deduction. Seventy-nine subjects from 24 families composed of, at the minimum, 3 were enrolled, and CASMA-trio was employed to determine whether an index subject with 2 SMN1 copies was a 2 + 0 carrier in these families. For the proof-of-principle, SMN2 2 + 0 was also analyzed. RESULTS: Among the 16 subjects with 2 SMN1 copies, CASMA-trio identified 5 subjects from 4 families as SMN1 2 + 0 carriers, which was consistent with pedigree analysis involving an affected proband. CASMA-trio also identified SMN2 2 + 0 in six out of 43 subjects with 2 SMN2 copies. Additionally, CASMA-trio successfully determined the distribution pattern of SMN1 and SMN2 genes on 2 alleles in all 79 subjects. CONCLUSIONS: CASMA-trio represents an effective and universal approach for SMN1 2 + 0 carriers screening, as it does not reply on the presence of an affected proband, certain single-nucleotide polymorphisms, ethnicity-specific haplotypes, or complicated single-nucleotide polymorphism analysis across 3 generations. Incorporating CASMA-trio into existing SMA carrier screening programs will greatly reduce residual risk ratio.

Morphological and evolutionary insights into the keystone element of the human foot's medial longitudinal arch.

The evolution of the medial longitudinal arch (MLA) is one of the most impactful adaptations in the hominin foot that emerged with bipedalism. When and how it evolved in the human lineage is still unresolved. Complicating the issue, clinical definitions of flatfoot in living Homo sapiens have not reached a consensus. Here we digitally investigate the navicular morphology of H. sapiens (living, archaeological, and fossil), great apes, and fossil hominins and its correlation with the MLA. A distinctive navicular shape characterises living H. sapiens with adult acquired flexible flatfoot, while the congenital flexible flatfoot exhibits a 'normal' navicular shape. All H. sapiens groups differentiate from great apes independently from variations in the MLA, likely because of bipedalism. Most australopith, H. naledi, and H. floresiensis navicular shapes are closer to those of great apes, which is inconsistent with a human-like MLA and instead might suggest a certain degree of arboreality. Navicular shape of OH 8 and fossil H. sapiens falls within the normal living H. sapiens spectrum of variation of the MLA (including congenital flexible flatfoot and individuals with a well-developed MLA). At the same time, H. neanderthalensis seem to be characterised by a different expression of the MLA.

Novel immune-related gene signature for risk stratification and prognosis prediction in ovarian cancer.

BACKGROUND: The immune system played a multifaceted role in ovarian cancer (OC) and was a significant mediator of ovarian carcinogenesis. Various immune cells and immune gene products played an integrated role in ovarian cancer (OC) progression, proved the significance of the immune microenvironment in prognosis. Therefore, we aimed to establish and validate an immune gene prognostic signature for OC patients' prognosis prediction. METHODS: Differently expressed Immune-related genes (DEIRGs) were identified in 428 OC and 77 normal ovary tissue specimens from 9 independent GEO datasets. The Cancer Genome Atlas (TCGA) cohort was used as a training cohort, Univariate Cox analysis was used to identify prognostic DEIRGs in TCGA cohort. Then, an immune gene-based risk model for prognosis prediction was constructed using the LASSO regression analysis, and validated the accuracy and stability of the model in 374 and 93 OC patients in TCGA training cohort and International Cancer Genome Consortium (ICGC) validation cohort respectively. Finally, the correlation among risk score model, clinicopathological parameters, and immune cell infiltration were analyzed. RESULTS: Five DEIRGs were identified to establish the immune gene signature and divided OC patients into the low- and high-risk groups. In TCGA and ICGC datasets, patients in the low-risk group showed a substantially higher survival rate than high-risk group. Receiver operating characteristic (ROC) curves, t-distributed stochastic neighbor embedding (t-SNE) analysis and principal component analysis (PCA) showed the good performance of the risk model. Clinicopathological correlation analysis proved the risk score model could serve as an independent prognostic factor in 2 independent datasets. CONCLUSIONS: The prognostic model based on immune-related genes can function as a superior prognostic indicator for OC patients, which could provide evidence for individualized treatment and clinical decision making.

Extracting the Synthetic Route of Pd-Based Catalysts in Methanol Steam Reforming from the Scientific Literature.

The structured material synthesis route is crucial for chemists in performing experiments and modern applications such as machine learning material design. With the exponential growth of the chemical literature in recent years, manual extraction from the published literature is time-consuming and labor-intensive. This study focuses on developing an automated method for extracting Pd-based catalyst synthesis routes from the chemical literature. First, a paragraph classification model based on regular expressions is employed to identify paragraphs that contain material synthesis processes. The identified paragraphs are verified using machine learning techniques. Second, natural language processing techniques are applied to automatically parse the material synthesis routes from the identified paragraphs, generate regularized flowcharts, and output structured data. Lastly, we utilized the structured data of the synthesis routes to train machine learning models and predict the performance of the materials. The extracted material entities include the product, preparation method, precursor, support, loading, synthesis operation, and operation condition. This method avoids extensive manual data annotation and improves the scientific literature information acquisition efficiency. The accuracy of the 11 material entities exceeds 80%, and the accuracy of the method, support, precursor, drying time, and reduction time exceeds 90%.

Built-in Electric Field-Induced Work Function Reduction in C-Co3O4 for Efficient Electrochemical Nitrogen Reduction.

Co3O4 is a highly selective catalyst for the electrochemical conversion of N2 to NH3. However, the large work function (WF) of Co3O4 leads to unsatisfactory activity. To address this issue, a strong built-in electric field (BIEF) was constructed in Co3O4 by doping C atoms (C-Co3O4) to reduce the WF for improving the electrocatalytic performance. C-Co3O4 exhibited a remarkable NH3 yield of 38.5 µg h-1 mgcat-1 and a promoted FE of 15.1% at -0.3 V vs RHE, which were 2.2 and 1.9 times higher than those of pure Co3O4, respectively. Kelvin probe force microscopy (KPFM), zeta potential, and ultraviolet photoelectron spectrometry (UPS) confirmed the formation of strong BIEF and WF reduction in C-Co3O4. Additionally, in situ Raman measurements and density functional theory (DFT) calculations revealed the relationship between BIEF and WF and provided insights into the reaction mechanism. Our work offers valuable guidance for the design and development of more efficient nitrogen reduction catalysts.

BNM-CDGNN: Batch Normalization Multilayer Perceptron Crystal Distance Graph Neural Network for Excellent-Performance Crystal Property Prediction.

Recently, in the field of crystal property prediction, the graph neural network (GNN) model has made rapid progress. The GNN model can effectively capture high-dimensional crystal features from the crystal structure, thereby achieving optimal performance in property prediction. However, the existing GNN model faces limitations in handling the hidden layer after the pooling layer, which restricts the training performance of the model. In the present research, we propose a novel GNN model called the batch normalization multilayer perceptron crystal distance graph neural network (BNM-CDGNN). BNM-CDGNN encodes the crystal's geometry structure only based on the distance vector between atoms. The graph convolutional layer utilizes the radial basis function as the attention mask, ensuring the crystal's rotation invariance and adding the geometric information on the crystal. Subsequently, the average pooling layer is connected after the convolutional layer to enhance the model's ability to learn precise information. BNM-CDGNN connects multiple hidden layers after the average pooling layers, and these layers are processed by the batch normalization layer. Finally, the fully connected layer maps the results to the target property. BNM-CDGNN significantly enhances the accuracy of crystal property prediction compared with previous baseline models such as SchNet, MPNN, CGCNN, MEGNet, and GATGNN.

[Action Mechanism of Bacillus on Microalgae During Nitrogen Metabolism in Urban Tailwater].

To improve deep denitrification of black and odorous water and improve the quality of surface water, we studied the characteristics of nitrogen metabolism and denitrification of urban tailwater by microalgae (Z), Bacillus (Y), and Bacillus microalgae (ZY). The results showed that there was a good removal effect of ammonia nitrogen of group Z and group ZY in urban tailwater. The degradation rate of both groups reached 95%. The best effect on the removal of nitrite nitrogen was of group Z in urban tailwater. The combined action of Bacillus and Micrococcus played a relatively strong and stable effect on the conversion of nitrite nitrogen to nitrate nitrogen in the nitrogen cycle reaction. Bacillus could effectively remove nitrate nitrogen and improve the removal efficiency of nitrate nitrogen by microalgae. Best removal effect of nitrate nitrogen was observed in group ZY in urban tailwater, with a degradation rate as high as 99%, in which the nitrate nitrogen was removed almost completely. The Bacteria with high proportions in Z were Chroococcidiopsis_PCC_7203 (24.38%), uncultured_bacterium-g_norank_f_A4b (23.65%), Exiguobacteriu (7.09%), Leptolyngbya_PCC-6306(9.41%), and Bacillus (1.99%). The bacteria with high proportions in ZY were Brevibacillus (22.94%), Clostridium (8.78%), and Bacillus (4.88%), and the proportion of Chroococcidiopsis_PCC_7203 was only 7.84% in ZY, which was considerably lower than that in Z samples. The conclusions were as follows:microalgae could effectively remove ammonia nitrogen in the system. Bacillus and microalgae had very good removal effect of ammonia nitrogen and nitrate nitrogen. During the nitrogen removal of black and odorous water by algae, the Bacillus inhibited the excessive growth of microalgae and prevented eutrophication and black odor in water. This study can provide data support for the deep treatment of urban tail water and prevention of surface water eutrophication.

Noninvasive Evaluation of Fetal Zygosity in Twin Pregnancies Involving a Binary Analysis of Single-Nucleotide Polymorphisms.

Twin pregnancy constitutes significant risks for maternal and fetal health, which is usually detected by ultrasound examination at early gestation. However, the imaging-based approach may not accurately identify all twins confounded by practical or clinical variables. The analysis of fetal cell-free DNA in noninvasive prenatal screening assays can completement the ultrasound method for twin detection, which differentiates fraternal or identical twins based on their distinct genotypes. Here, a new noninvasive prenatal screening employing high-coverage next-generation sequencing for targeted nucleotide polymorphisms was developed for detection of zygosity and determination of fetal fraction in twin pregnancies. This method utilizes a binary analysis of both the number and allelic fraction of fetus-specific single-nucleotide polymorphisms to infer the zygosity. In 323 samples collected from 215 singleton, 90 dizygotic, and 18 monozygotic twin pregnancies, all 90 dizygotic twins were correctly detected, with a 100% sensitivity and a 100% specificity. In addition, this method can detect complex pregnancies, such as egg donors, contamination, and twins with complete hydatidiform mole. The fetus-specific fetal fraction change was monitored in nine dizygotic twin pregnancies, which demonstrated highly variable dynamics of fetal cell-free DNA turnover up to 7 weeks after twin reduction. Overall, this study provides a new noninvasive prenatal screening strategy for the accurate identification of twin zygosity and quantification of fetal fraction, which has important clinical implications for the management of twin pregnancies.

Mining Prognostic Biomarkers of Thyroid Cancer Patients Based on the Immune-Related Genes and Development of a Reliable Prognostic Risk Model.

Purpose: Tumor immunity serves an essential role in the occurrence and development of thyroid cancer (THCA). The aim of this study is to establish an immune-related prognostic model for THCA patients by using immune-related genes (IRGs). Methods: Wilcox test was used to screen the differentially expressed immune-related genes (DEIRGs) in THCA and normal tissues, then the DEIRGs related to prognosis were identified using univariate Cox regression analysis. According to The Cancer Genome Atlas (TCGA) cohort, we developed a least absolute shrinkage and selection operator (LASSO) regression prognostic model and performed validation analyses regard to the predictive value of the model in internal (TCGA) and external (International Cancer Genome Consortium) cohorts respectively. Finally, we analyzed the correlation among the prognostic model, clinical variables, and immune cell infiltration. Results: Eighty-two of 2,498 IRGs were differentially expressed between THCA and normal tissues, and 18 of them were related to prognosis. LASSO Cox regression analysis identified seven DEIRGs with the greatest prognostic value to construct the prognostic model. The risk model showed high predictive value for the survival of THCA in two independent cohorts. The risk score according to the risk model was positively associated with poor survival and the infiltration levels of immune cells, it can evaluate the prognosis of THCA patients independent of any other clinicopathologic feature. The prognostic value and genetic alternations of seven risk genes were evaluated separately. Conclusion: Our study established and verified a dependable prognostic model associated with immune for THCA, both the identified IRGs and immune-related risk model were clinically significant, which is conducive to promoting individualized immunotherapy against THCA.