Machine Learning-Derived Active Sleep as an Early Predictor of White Matter Development in Preterm Infants.

White matter dysmaturation is commonly seen in preterm infants admitted to the neonatal intensive care unit (NICU). Animal research has shown that active sleep is essential for early brain plasticity. This study aimed to determine the potential of active sleep as an early predictor for subsequent white matter development in preterm infants. Using heart and respiratory rates routinely monitored in the NICU, we developed a machine learning-based automated sleep stage classifier in a cohort of 25 preterm infants (12 females). The automated classifier was subsequently applied to a study cohort of 58 preterm infants (31 females) to extract active sleep percentage over 5-7 consecutive days during 29-32 weeks of postmenstrual age. Each of the 58 infants underwent high-quality T2-weighted magnetic resonance brain imaging at term-equivalent age, which was used to measure the total white matter volume. The association between active sleep percentage and white matter volume was examined using a multiple linear regression model adjusted for potential confounders. Using the automated classifier with a superior sleep classification performance [mean area under the receiver operating characteristic curve (AUROC) = 0.87, 95% CI 0.83-0.92], we found that a higher active sleep percentage during the preterm period was significantly associated with an increased white matter volume at term-equivalent age [ß = 0.31, 95% CI 0.09-0.53, false discovery rate (FDR)-adjusted p-value = 0.021]. Our results extend the positive association between active sleep and early brain development found in animal research to human preterm infants and emphasize the potential benefit of sleep preservation in the NICU setting.

RS-FISH: precise, interactive, fast, and scalable FISH spot detection.

Fluorescent in-situ hybridization (FISH)-based methods extract spatially resolved genetic and epigenetic information from biological samples by detecting fluorescent spots in microscopy images, an often challenging task. We present Radial Symmetry-FISH (RS-FISH), an accurate, fast, and user-friendly software for spot detection in two- and three-dimensional images. RS-FISH offers interactive parameter tuning and readily scales to large datasets and image volumes of cleared or expanded samples using distributed processing on workstations, clusters, or the cloud. RS-FISH maintains high detection accuracy and low localization error across a wide range of signal-to-noise ratios, a key feature for single-molecule FISH, spatial transcriptomics, or spatial genomics applications.

PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery.

PandaOmics is a cloud-based software platform that applies artificial intelligence and bioinformatics techniques to multimodal omics and biomedical text data for therapeutic target and biomarker discovery. PandaOmics generates novel and repurposed therapeutic target and biomarker hypotheses with the desired properties and is available through licensing or collaboration. Targets and biomarkers generated by the platform were previously validated in both in vitro and in vivo studies. PandaOmics is a core component of Insilico Medicine's Pharma.ai drug discovery suite, which also includes Chemistry42 for the de novo generation of novel small molecules, and inClinico─a data-driven multimodal platform that forecasts a clinical trial's probability of successful transition from phase 2 to phase 3. In this paper, we demonstrate how the PandaOmics platform can efficiently identify novel molecular targets and biomarkers for various diseases.

A multi-task learning model using RR intervals and respiratory effort to assess sleep disordered breathing.

BACKGROUND: Sleep-disordered breathing (SDB) affects a significant portion of the population. As such, there is a need for accessible and affordable assessment methods for diagnosis but also case-finding and long-term follow-up. Research has focused on exploiting cardiac and respiratory signals to extract proxy measures for sleep combined with SDB event detection. We introduce a novel multi-task model combining cardiac activity and respiratory effort to perform sleep-wake classification and SDB event detection in order to automatically estimate the apnea-hypopnea index (AHI) as severity indicator. METHODS: The proposed multi-task model utilized both convolutional and recurrent neural networks and was formed by a shared part for common feature extraction, a task-specific part for sleep-wake classification, and a task-specific part for SDB event detection. The model was trained with RR intervals derived from electrocardiogram and respiratory effort signals. To assess performance, overnight polysomnography (PSG) recordings from 198 patients with varying degree of SDB were included, with manually annotated sleep stages and SDB events. RESULTS: We achieved a Cohen's kappa of 0.70 in the sleep-wake classification task, corresponding to a Spearman's correlation coefficient (R) of 0.830 between the estimated total sleep time (TST) and the TST obtained from PSG-based sleep scoring. Combining the sleep-wake classification and SDB detection results of the multi-task model, we obtained an R of 0.891 between the estimated and the reference AHI. For severity classification of SBD groups based on AHI, a Cohen's kappa of 0.58 was achieved. The multi-task model performed better than a single-task model proposed in a previous study for AHI estimation, in particular for patients with a lower sleep efficiency (R of 0.861 with the multi-task model and R of 0.746 with single-task model with subjects having sleep efficiency < 60%). CONCLUSION: Assisted with automatic sleep-wake classification, our multi-task model demonstrated proficiency in estimating AHI and assessing SDB severity based on AHI in a fully automatic manner using RR intervals and respiratory effort. This shows the potential for improving SDB screening with unobtrusive sensors also for subjects with low sleep efficiency without adding additional sensors for sleep-wake detection.

Characterising the motion and cardiorespiratory interaction of preterm infants can improve the classification of their sleep state.

AIM: This study aimed to classify quiet sleep, active sleep and wake states in preterm infants by analysing cardiorespiratory signals obtained from routine patient monitors. METHODS: We studied eight preterm infants, with an average postmenstrual age of 32.3 ± 2.4 weeks, in a neonatal intensive care unit in the Netherlands. Electrocardiography and chest impedance respiratory signals were recorded. After filtering and R-peak detection, cardiorespiratory features and motion and cardiorespiratory interaction features were extracted, based on previous research. An extremely randomised trees algorithm was used for classification and performance was evaluated using leave-one-patient-out cross-validation and Cohen's kappa coefficient. RESULTS: A sleep expert annotated 4731 30-second epochs (39.4 h) and active sleep, quiet sleep and wake accounted for 73.3%, 12.6% and 14.1% respectively. Using all features, and the extremely randomised trees algorithm, the binary discrimination between active and quiet sleep was better than between other states. Incorporating motion and cardiorespiratory interaction features improved the classification of all sleep states (kappa 0.38 ± 0.09) than analyses without these features (kappa 0.31 ± 0.11). CONCLUSION: Cardiorespiratory interactions contributed to detecting quiet sleep and motion features contributed to detecting wake states. This combination improved the automated classifications of sleep states.

Risk factors analysis of lateral cervical lymph node metastasis in papillary thyroid carcinoma: a retrospective study of 830 patients.

OBJECTIVE: The aim of this study is to investigate the risk factors for lateral cervical lymph node metastasis in papillary thyroid carcinoma (PTC). METHODS: Clinicopathological data (age, gender, Hashimoto's thyroiditis, preoperative circulating tumor cells (CTCs), multifocal, maximum lesion diameter, invaded capsule, T stage, and lymph node metastasis) of 830 PTC patients diagnosed and treated in Meizhou People's Hospital from June 2021 to April 2023 were collected. The related factors of lateral cervical lymph node metastasis were analyzed. RESULTS: There were 334 (40.2%), and 103 (12.4%) PTC patients with central lymph node metastasis, and lateral cervical lymph node metastasis, respectively. Compared with patients without lateral cervical lymph node metastasis, PTC patients with lateral cervical lymph node metastasis had a higher proportion of multifocal, maximum lesion diameter > 1 cm, invaded capsule, T3-T4 stage. Regression logistic analysis showed that male (odds ratio (OR): 2.196, 95% confidence interval (CI): 1.279-3.769, p = 0.004), age < 55 years old (OR: 2.057, 95% CI: 1.062-3.988, p = 0.033), multifocal (OR: 2.759, 95% CI: 1.708-4.458, p < 0.001), maximum lesion diameter > 1 cm (OR: 5.408, 95% CI: 3.233-9.046, p < 0.001), T3-T4 stage (OR: 2.396, 95% CI: 1.241-4.626, p = 0.009), and invaded capsule (OR: 2.051, 95% CI: 1.208-3.480, p = 0.008) were associated with lateral cervical lymph node metastasis. CONCLUSIONS: Male, age < 55 years old, multifocal, maximum lesion diameter > 1 cm, T3-T4 stage, and invaded capsule were independent risk factors for lateral cervical lymph node metastasis in PTC.

Development and Validation of an Interpretable Conformal Predictor to Predict Sepsis Mortality Risk: Retrospective Cohort Study.

BACKGROUND: Early and reliable identification of patients with sepsis who are at high risk of mortality is important to improve clinical outcomes. However, 3 major barriers to artificial intelligence (AI) models, including the lack of interpretability, the difficulty in generalizability, and the risk of automation bias, hinder the widespread adoption of AI models for use in clinical practice. OBJECTIVE: This study aimed to develop and validate (internally and externally) a conformal predictor of sepsis mortality risk in patients who are critically ill, leveraging AI-assisted prediction modeling. The proposed approach enables explaining the model output and assessing its confidence level. METHODS: We retrospectively extracted data on adult patients with sepsis from a database collected in a teaching hospital at Beth Israel Deaconess Medical Center for model training and internal validation. A large multicenter critical care database from the Philips eICU Research Institute was used for external validation. A total of 103 clinical features were extracted from the first day after admission. We developed an AI model using gradient-boosting machines to predict the mortality risk of sepsis and used Mondrian conformal prediction to estimate the prediction uncertainty. The Shapley additive explanation method was used to explain the model. RESULTS: A total of 16,746 (80%) patients from Beth Israel Deaconess Medical Center were used to train the model. When tested on the internal validation population of 4187 (20%) patients, the model achieved an area under the receiver operating characteristic curve of 0.858 (95% CI 0.845-0.871), which was reduced to 0.800 (95% CI 0.789-0.811) when externally validated on 10,362 patients from the Philips eICU database. At a specified confidence level of 90% for the internal validation cohort the percentage of error predictions (n=438) out of all predictions (n=4187) was 10.5%, with 1229 (29.4%) predictions requiring clinician review. In contrast, the AI model without conformal prediction made 1449 (34.6%) errors. When externally validated, more predictions (n=4004, 38.6%) were flagged for clinician review due to interdatabase heterogeneity. Nevertheless, the model still produced significantly lower error rates compared to the point predictions by AI (n=1221, 11.8% vs n=4540, 43.8%). The most important predictors identified in this predictive model were Acute Physiology Score III, age, urine output, vasopressors, and pulmonary infection. Clinically relevant risk factors contributing to a single patient were also examined to show how the risk arose. CONCLUSIONS: By combining model explanation and conformal prediction, AI-based systems can be better translated into medical practice for clinical decision-making.

Maternal Hypermethylated Genes Contribute to Intrauterine Growth Retardation of Piglets in Rongchang Pigs.

The placenta is a crucial determinant of fetal survival, growth, and development. Deficiency in placental development directly causes intrauterine growth retardation (IUGR). IUGR can lead to fetal growth restriction and an increase in the mortality rate. The genetic mechanisms underlying IUGR development, however, remain unclear. In the present study, we integrated whole-genome DNA methylation and transcriptomic analyses to determine distinct gene expression patterns in various placental tissues to identify pivotal genes that are implicated with IUGR development. By performing RNA-sequencing analysis, 1487 differentially expressed genes (DEGs), with 737 upregulated and 750 downregulated genes, were identified in IUGR pigs (H_IUGR) compared with that in normal birth weight pigs (N_IUGR) (p < 0.05); furthermore, 77 miRNAs, 1331 lncRNAs, and 61 circRNAs were differentially expressed. The protein-protein interaction network analysis revealed that among these DEGs, the genes GNGT1, ANXA1, and CDC20 related to cellular developmental processes and blood vessel development were the key genes associated with the development of IUGR. A total of 495,870 differentially methylated regions were identified between the N_IUGR and H_IUGR groups, which included 25,053 differentially methylated genes (DMEs); moreover, the overall methylation level was higher in the H_IUGR group than in the N_IUGR group. Combined analysis showed an inverse correlation between methylation levels and gene expression. A total of 1375 genes involved in developmental processes, tissue development, and immune system regulation exhibited methylation differences in gene expression levels in the promoter regions and gene ontology regions. Five genes, namely, ANXA1, ADM, NRP2, SHH, and SMAD1, with high methylation levels were identified as potential contributors to IUGR development. These findings provide valuable insights that DNA methylation plays a crucial role in the epigenetic regulation of gene expression and mammalian development and that DNA-hypermethylated genes contribute to IUGR development in Rongchang pigs.

Hairpin Switches-Based Isothermal Transcription Amplification for Simple, Sensitivity Detection of MicroRNA.

The ability to simply, selectively, and sensitively detect low numbers of miRNAs in clinical samples is highly valuable but remains a challenge. In this work, we present a novel miRNA detection system by using the elaborately designed hairpin switch, where the T7 primer, template, target recognize sequence, and light-up RNA aptamer template are edited and embedded in one single-stranded DNA hairpin structure. In the beginning, the hairpin switch maintained the hairpin structure 1, in which the ds promoter of T7 polymerase was disrupted, thus the transcription reaction of T7 polymerase was inhibited. After binding to the target, the hairpin switch 1 was unfolded and turned to the hairpin structure 2. This switch initiates the in vitro T7 transcription reaction, producing plenty of RNA transcripts containing RNA aptamers. Consequently, transcribed tremendous RNA aptamers lighted up the fluorophore for quantitative analysis. Compared with the existing T7 polymerase-based amplification system, this strategy exhibits several advantages, including simplicity, convenience, and high selectivity and sensitivity. The experimental results demonstrated that we could achieve the quantification of miRNA in buffer and complex biological samples.

Identification of genetic loci associated with five agronomic traits in alfalfa using multi-environment trials.

KEY MESSAGE: The use of multi-environment trials to test yield-related traits in a diverse alfalfa panel allowed to find multiple molecular markers associated with complex agronomic traits. Yield is one of the most important target traits in alfalfa breeding; however, yield is a complex trait affected by genetic and environmental factors. In this study, we used multi-environment trials to test yield-related traits in a diverse panel composed of 200 alfalfa accessions and varieties. Phenotypic data of maturity stage measured as mean stage by count (MSC), dry matter content, plant height (PH), biomass yield (Yi), and fall dormancy (FD) were collected in three locations in Idaho, Oregon, and Washington from 2018 to 2020. Single-trial and stagewise analyses were used to obtain estimated trait means of entries by environment. The plants were genotyped using a genotyping by sequencing approach and obtained a genotypic matrix with 97,345 single nucleotide polymorphisms. Genome-wide association studies identified a total of 84 markers associated with the traits analyzed. Of those, 29 markers were in noncoding regions and 55 markers were in coding regions. Ten significant SNPs at the same locus were associated with FD and they were linked to a gene annotated as a nuclear fusion defective 4-like (NFD4). Additional SNPs associated with MSC, PH, and Yi were annotated as transcription factors such as Cysteine3Histidine (C3H), Hap3/NF-YB family, and serine/threonine-protein phosphatase 7 proteins, respectively. Our results provide insight into the genetic factors that influence alfalfa maturity, yield, and dormancy, which is helpful to speed up the genetic gain toward alfalfa yield improvement.

Biochar amendment reassembles microbial community in a long-term phosphorus fertilization paddy soil.

This study investigates the effect of biochar amendment on microbial community structure and soil nutrient status in paddy soil that has been fertilized for an extended period of time, shedding light on sustainable agricultural practices. A 90-day incubation period revealed that biochar amendment, as opposed to long-term fertilization, significantly influenced the physicochemical properties and microbial composition of the soil. The microcosm experiment conducted using six treatments analyzed soil samples from a long-term rice ecosystem. We employed microbial biomarkers (phospholipid fatty acids, PLFAs; isoprenoid and branched glycerol dialkyl glycerol tetraethers, iGDGTs and brGDGTs; DNA) to assess microbial biomass and community structure. Biochar addition led to a decrease in PLFA biomass (15-32%) and archaeal iGDGT abundance (14-43%), while enhancing bacterial brGDGT abundance by 15-77%. Intact biochar increased archaeal and bacterial diversity, though fungal diversity remained unchanged. However, acid-washed biochar did not result in a uniform microbial diversity response. The abundance of various microbial taxa was changed by biochar amendment, including Crenarchaeota, Proteobacteria, Nitrospira, Basidiomycota, Halobacterota, Chloroflexi, Planctomycetota, and Ascomycota. Soil NH4+-N was found as the primary environmental factor impacting the composition of archaea, bacteria, and fungus in this study. These findings imply that the addition of biochar has a quick influence on the structure and activity of microbial communities, with fungi possibly having a critical role in acid paddy soil. This study contributes valuable knowledge for developing sustainable agricultural practices that promote healthy soil ecosystems. KEY POINTS: • Biochar type and phosphorus fertilization demonstrated an interactive effect on the diversity of archaea, but no such effect was observed for bacteria and fungi. • Soil fungi contribute to approximately 20% of the total phospholipid fatty acid (PLFA) content. • Biochar, especially acid-washed rice straw biochar, increases glucose metabolism in bacteria and archaea and decreases saprophytic fungi.

Ultra-Wideband Radar for Simultaneous and Unobtrusive Monitoring of Respiratory and Heart Rates in Early Childhood: A Deep Transfer Learning Approach.

Unobtrusive monitoring of children's heart rate (HR) and respiratory rate (RR) can be valuable for promoting the early detection of potential health issues, improving communication with healthcare providers and reducing unnecessary hospital visits. A promising solution for wireless vital sign monitoring is radar technology. This paper presents a novel approach for the simultaneous estimation of children's RR and HR utilizing ultra-wideband (UWB) radar using a deep transfer learning algorithm in a cohort of 55 children. The HR and RR are calculated by processing radar signals via spectrogram from time epochs of 10 s (25 sample length of hamming window with 90% overlap) and then transforming the resultant representation into 2-dimensional images. These images were fed into a pre-trained Visual Geometry Group-16 (VGG-16) model (trained on ImageNet dataset), with weights of five added layers fine-tuned using the proposed data. The prediction on the test data achieved a mean absolute error (MAE) of 7.3 beats per minute (BPM < 6.5% of average HR) and 2.63 breaths per minute (BPM < 7% of average RR). We also achieved a significant Pearson's correlation of 77% and 81% between true and extracted for HR and RR, respectively. HR and RR samples are extracted every 10 s.

Comparison of the Clinical Effect of Double Eyelid Blepharoplasty with the Orbital Septum Method and the Classical Method.

OBJECTIVE: To observe the clinical effect between orbital septum incision and classical incision of double eyelid plasty. METHODS: We retrospectively analyzed 381 patients who underwent double eyelid blepharoplasty in the Department of Plastic and Laser Cosmetology of Hunan Provincial People's Hospital from January 2019 to December 2019. The patients were divided into two groups according to different surgical methods: group A (n = 146) received the classical method and group B (n = 235) received the orbital septum method. The incidence of early postoperative complications, scar depression from 6 months to 1 year after the operation, the condition of 'meat strip' (the accumulation of soft tissue in front of the tarsal plate after double eyelid surgery, including skin, muscle, and fascia fat, results in a hypertrophic appearance of the upper eyelid) below the double eyelid line, and the symmetry of double eyelids were analyzed and evaluated. RESULTS: The total number of early postoperative complications in group A was seven cases (incidence rate: approximately 4.80%), and the total number of early postoperative complications in group B was two cases (incidence rate: approximately 0.85%), with a statistically significant difference (P < 0.05). The degree of scar depression in group B was significantly lighter than that in group A from 6 months to 1 year after the operation (P < 0.05). The score of 'meat strip' below the double eyelid line in group B was significantly lighter than that in group A (P < 0.05). The symmetry of double eyelids in group B was better than that in group A (P < 0.05) CONCLUSION: Compared to the classical double eyelid method, the orbital septum method has the advantages of reducing early postoperative complications, reducing the severity of the scar, slighting the 'meat strip,' and improving symmetry, which results in higher postoperative satisfaction LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors https://www.springer.com/00266 .

Evaluation of the Genetic Diversity and Population Structure of Four Native Pig Populations in Gansu Province.

Indigenous pig populations, including Bamei pigs (BM), Hezuo pigs (HZ), Huixian Qingni Black pigs (HX), and Minxian Black pigs (MX) in Gansu Province, live in a particular climate and a relatively closed geographical environment. These local pig breeds are characterized by excellent characteristics (e.g., cold tolerance, robust disease resistance, and superior meat quality). In the past few years, pig populations in Gansu Province have decreased significantly because of their poor lean meat percentage, high fat content, and slow growth rate. Maintaining the diversity of these four breeds can act as a source of new alleles to be incorporated into commercial breeds which are more susceptible to disease and less adaptable to changing conditions because of inbreeding. Genomic data analysis is adequate for determining the genetic diversity and livestock breeding population structure, even in local pig populations. However, the genetic diversity and population structure of the four native pig populations in Gansu Province are still unknown. Thus, we used "Zhongxin-I" porcine chip for the SNP detection of 102 individuals living on four pig conservation farms. A total of 57,466 SNPs were identified among the four pig breeds. The linkage disequilibrium (LD) plot showed that MX had the highest level of LD, followed by BM, HZ, and HX. The observed heterozygosity (Ho) in all four populations was higher than the expected heterozygosity (He). A principal component analysis (PCA) demonstrated that the four local pig populations were isolated. The identity displayed by the state matrix and G matrix heat map results indicated that small numbers of individuals among the four pig breeds had a high genetic distance and weak genetic relationships. The results of the population genetic structure of BM, HZ, HX, and MX pigs showed a slight genetic diversity loss. Our findings enabled us to better understand the genome characteristics of these four indigenous pig populations, which will provide novel insights for the future germplasm conservation and utilization of these indigenous pig populations.

Structural Reorganization in Two Alfalfa Mitochondrial Genome Assemblies and Mitochondrial Evolution in Medicago Species.

Plant mitochondria are crucial for species evolution, phylogenetics, classification, and identification as maternal genetic material. However, the presence of numerous repetitive sequences, complex structures, and a low number of genes in the mitochondrial genome has hindered its complete assembly and related research endeavors. In this study, we assembled two mitochondrial genomes of alfalfa varieties of Zhongmu No.1 (299,123 bp) and Zhongmu No.4 (306,983 bp), based on a combination of PacBio, Illumina, and Hi-C sequences. The comparison of genome assemblies revealed that the same number of mitochondrial genes, including thirty-three protein-coding genes, sixteen tRNA genes, and three rRNA genes existed in the two varieties. Additionally, large fragments of repetitive sequences were found underlying frequent mitochondrial recombination events. We observed extensive transfer of mitochondrial fragments into the nuclear genome of Zhongmu No.4. Analysis of the cox1 and rrn18s genes in 35 Medicago accessions revealed the presence of population-level deletions and substitutions in the rrn18s gene. We propose that mitochondrial structural reorganizations may contribute to alfalfa evolution.

Lactobacillus sp. participated in the adaptation of Rongchang piglets to cold stress.

Rongchang piglets were easily induced to cold stress and diarrhoea in the winter when raised in an open hog house. However, they also gradually recovered under mid-cold stress. Other studies have suggested gut microbiome might be involved in the host energy metabolism to relieve stress. To study how to adapt Rongchang piglets to cold stress by gut microbiome, thirty Rongchang piglets were randomly divided into a mild cold stress group and a control group for 30 consecutive days. The findings revealed that the piglets had low growth performance and a high diarrhoea rate and mortality rate during the first half of the cold treatment, but subsequently stabilised. The level of cortisol (COR) also displayed a similar trend. In the mild cold stress group, the relative abundance of Muribaculaceae significantly increased on day 15, and the predominant bacterial on day 30 was Lactobacillus sp. Our results indicated that the Rongchang piglet's production performance and health were impaired at the start of the mild cold stress. However, as time passed, the body could progressively adapt to the low temperature, and Lactobacillus sp. participated in this process. This study provides new insight into how to alleviate health damage caused by cold stress.

Single-Molecule Mechanical Analysis of Strand Invasion in Human Telomere DNA.

Telomeres are essential chromosome end capping structures that safeguard the genome from dangerous DNA processing events. DNA strand invasion occurs during vital transactions at telomeres, including telomere length maintenance by the alternative lengthening of telomeres (ALT) pathway. During telomeric strand invasion, a single-stranded guanine-rich (G-rich) DNA invades at a complementary duplex telomere repeat sequence, forming a displacement loop (D-loop) in which the displaced DNA consists of the same G-rich sequence as the invading single-stranded DNA. Single-stranded G-rich telomeric DNA readily folds into stable, compact, structures called G-quadruplexes (GQs) in vitro and is anticipated to form within the context of a D-loop; however, evidence supporting this hypothesis is lacking. Here, we report a magnetic tweezers assay that permits the controlled formation of telomeric D-loops (TDLs) within uninterrupted duplex human telomere DNA molecules of physiologically relevant lengths. Our results are consistent with a model wherein the displaced single-stranded DNA of a TDL fold into a GQ. This study provides new insight into telomere structure and establishes a framework for the development of novel therapeutics designed to target GQs at telomeres in cancer cells.

Designing a CRISPR/Cas12a- and Au-Nanobeacon-Based Diagnostic Biosensor Enabling Direct, Rapid, and Sensitive miRNA Detection.

Direct, rapid, sensitive, and selective detection of nucleic acids in complex biological fluids is crucial for medical early diagnosis. We herein combine the trans-cleavage ability of clustered regularly interspaced short palindromic repeats (CRISPR)/Cas12a with Au-nanobeacon to establish a CRISPR-based biosensor, providing rapid miRNA detection with high speed and attomolar sensitivity. In this strategy, we first report that the trans-cleavage activity of CRISPR/cas12a, which was previously reported to be triggered only by target ssDNA or dsDNA, can be activated by the target miRNA directly. Therefore, this method is direct, i.e., does not need the conversion of miRNA into its complementary DNA (cDNA). Meanwhile, as compared to the traditional ssDNA reporters and molecular beacon (MB) reporters, the Au-nanobeacon reporters exhibit improved reaction kinetics and sensitivity. In this assay, the miRNA-21 could be detected with very high sensitivity in only 5 min. Finally, the proposed strategy enables rapid, sensitive, and selective miRNA determination in complex biological samples, providing a potential tool for medical early diagnosis.

Genetic-variant hotspots and hotspot clusters in the human genome facilitating adaptation while increasing instability.

BACKGROUND: Genetic variants, underlining phenotypic diversity, are known to distribute unevenly in the human genome. A comprehensive understanding of the distributions of different genetic variants is important for insights into genetic functions and disorders. METHODS: Herein, a sliding-window scan of regional densities of eight kinds of germline genetic variants, including single-nucleotide-polymorphisms (SNPs) and four size-classes of copy-number-variations (CNVs) in the human genome has been performed. RESULTS: The study has identified 44,379 hotspots with high genetic-variant densities, and 1135 hotspot clusters comprising more than one type of hotspots, accounting for 3.1% and 0.2% of the genome respectively. The hotspots and clusters are found to co-localize with different functional genomic features, as exemplified by the associations of hotspots of middle-size CNVs with histone-modification sites, work with balancing and positive selections to meet the need for diversity in immune proteins, and facilitate the development of sensory-perception and neuroactive ligand-receptor interaction pathways in the function-sparse late-replicating genomic sequences. Genetic variants of different lengths co-localize with retrotransposons of different ages on a "long-with-young" and "short-with-all" basis. Hotspots and clusters are highly associated with tumor suppressor genes and oncogenes (p < 10-10), and enriched with somatic tumor CNVs and the trait- and disease-associated SNPs identified by genome-wise association studies, exceeding tenfold enrichment in clusters comprising SNPs and extra-long CNVs. CONCLUSIONS: In conclusion, the genetic-variant hotspots and clusters represent two-edged swords that spearhead both positive and negative genomic changes. Their strong associations with complex traits and diseases also open up a potential "Common Disease-Hotspot Variant" approach to the missing heritability problem.

Comprehensive Analysis of Tripterine Anti-Ovarian Cancer Effects Using Weighted Gene Co-Expression Network Analysis and Molecular Docking.

BACKGROUND Ovarian cancer has the highest mortality of gynecological cancers worldwide. The aim of this study was to identify the role of tripterine against ovarian cancer. MATERIAL AND METHODS GSE18520 and GSE12470 data sets were downloaded from the GEO database. WGCNA was used to analyze gene modules and hub genes related to ovarian cancer. These hub genes were intersected with tripterine targets, and GO and KEGG enrichment analyses were performed. HPA and GEPIA determined the expression of tripterine anti-ovarian hub genes in tumor tissues. Kaplan-Meier plotter was used to explore the role of hub genes in ovarian cancer prognosis. AutoDock was used to conduct molecular docking of tripterine and hub genes to observe whether the combination was stable. RESULTS By differential analysis of gene expression and the construction of WGCNA co-expression network, 5 hub genes, ARHGAP11A, MUC1, HBB, RUNX1T1, and FUT8, were screened by module gene screening. Seven biological processes and 20 KEGG-related pathways were obtained by gene enrichment. The expression of tripterine anti-ovarian hub genes ARHGAP11A, MUC1, and FUT8 were obtained by HPA and GEPIA. Using Kaplan-Meier plotter, the survival of ovarian cancer was negatively correlated with ARHGAP11A, MUC1, and FUT8. Molecular docking showed the combination of tripterine and FUT8 was most stable, having the greatest potential role. CONCLUSIONS Tripterine may be involved in megakaryocyte development and platelet production through potential genes ARHGAP11A, MUC1, HBB, RUNX1T1, and FUT8 and may have an anti-ovarian cancer effect in immune factors signaling, transporting and exchanging oxygen pathways, and autophagy pathways, through these 5 key genes.