Characterizing the microbiota of cleft lip and palate patients: a comprehensive review

Orofacial cleft disorders, including cleft lip and/or palate (CL/P), are one of the most frequently-occurring congenital disorders worldwide. The health issues of patients with CL/P encompass far more than just their anatomic anomaly, as patients with CL/P are prone to having a high incidence of infectious diseases. While it has been previously established that the oral microbiome of patients with CL/P differs from that of unaffected patients, the exact nature of this variance, including the relevant bacterial species, has not been fully elucidated; likewise, examination of anatomic locations besides the cleft site has been neglected. Here, we intended to provide a comprehensive review to highlight the significant microbiota differences between CL/P patients and healthy subjects in various anatomic locations, including the teeth inside and adjacent to the cleft, oral cavity, nasal cavity, pharynx, and ear, as well as bodily fluids, secretions, and excretions. A number of bacterial and fungal species that have been proven to be pathogenic were found to be prevalently and/or specifically detected in CL/P patients, which can benefit the development of CL/P-specific microbiota management strategies

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

A first update on mapping the human genetic architecture of COVID-19

peer reviewe

Identifying Complex Trait-Related Genes Via Regulation-Informed Gene-Based Analyses

While our understanding of dysregulated genes is essential for improvement of clinical care, the majority of complex trait-associated genetic variants (\u3e90%) are located in noncoding regions of the human genome. Also connecting noncoding genetic variants to downstream affected genes is challenging. On the other hand, noncoding elements can regulate genes. Regulatory elements such as expression quantitative trait loci (eQTLs) provides a potential means to link noncoding genetic variants to affected genes and to explore complex disease mechanisms. Transcriptome-wide association studies (TWAS) is a popular algorithm that exploits eQTLs to prioritize transcriptionally regulated genes from genome-wide association studies (GWAS). Transcriptional regulation is tissue-specific. However, it was unclear how biological properties of eQTLs and gene expression levels will affect the power of different TWAS methods. To answer this question, I designed and developed a novel data simulation framework that efficiently simulates variant, gene, and disease data according to designed relationships across multiple tissues simultaneously. The simulation showed that TWAS performance differed for tissue-specific genes and for genes that were expressed across all tissues. Thus, I put forth a tissue specificity-aware TWAS (TSA-TWAS) framework, validated its utility in clinical trials data, and promoted further suggestions for future TWAS regarding varied scenarios. Centralized biobanks, such as Penn Medicine Biobank (PMBB), and Electronic Medical Records and Genomics (eMERGE) network, have collected a plethora of biospecimen and disease diagnosis; and recruited participants of varied genetic ancestries. However, it is not clear how disease susceptibility genes are like for different genetic ancestries and categories of diseases. Based on the simulation of the thesis part one, I designed a framework that applies TWAS and other data integrative methods on multi-ancestry EHR-linked biobanks to identify ancestry-specific and cross-ancestry gene-disease associations under a discovery (eMERGE III network) and replication (PMBB) study design. This study characterized a multi-ancestry gene-disease connection landscape. This thesis contributes (1) a novel multi-tissue variant-gene-trait simulation framework, comprehensive evaluation of TWAS and (2) a multi-ancestry gene-disease connection landscape. Together, the thesis helps improve the understanding of genetically regulated genes underlying complex diseases and promote translation of basic science discoveries to clinical health care

From GWAS to Gene: Transcriptome-Wide Association Studies and Other Methods to Functionally Understand GWAS Discoveries

Since their inception, genome-wide association studies (GWAS) have identified more than a hundred thousand single nucleotide polymorphism (SNP) loci that are associated with various complex human diseases or traits. The majority of GWAS discoveries are located in non-coding regions of the human genome and have unknown functions. The valley between non-coding GWAS discoveries and downstream affected genes hinders the investigation of complex disease mechanism and the utilization of human genetics for the improvement of clinical care. Meanwhile, advances in high-throughput sequencing technologies reveal important genomic regulatory roles that non-coding regions play in the transcriptional activities of genes. In this review, we focus on data integrative bioinformatics methods that combine GWAS with functional genomics knowledge to identify genetically regulated genes. We categorize and describe two types of data integrative methods. First, we describe fine-mapping methods. Fine-mapping is an exploratory approach that calibrates likely causal variants underneath GWAS signals. Fine-mapping methods connect GWAS signals to potentially causal genes through statistical methods and/or functional annotations. Second, we discuss gene-prioritization methods. These are hypothesis generating approaches that evaluate whether genetic variants regulate genes via certain genetic regulatory mechanisms to influence complex traits, including colocalization, mendelian randomization, and the transcriptome-wide association study (TWAS). TWAS is a gene-based association approach that investigates associations between genetically regulated gene expression and complex diseases or traits. TWAS has gained popularity over the years due to its ability to reduce multiple testing burden in comparison to other variant-based analytic approaches. Multiple types of TWAS methods have been developed with varied methodological designs and biological hypotheses over the past 5 years. We dive into discussions of how TWAS methods differ in many aspects and the challenges that different TWAS methods face. Overall, TWAS is a powerful tool for identifying complex trait-associated genes. With the advent of single-cell sequencing, chromosome conformation capture, gene editing technologies, and multiplexing reporter assays, we are expecting a more comprehensive understanding of genomic regulation and genetically regulated genes underlying complex human diseases and traits in the future.</jats:p

Analysis of academic procrastination in professional students of a tertiary training programme

Academic procrastination is a common behavior among tertiary students. In particular, parttime adult students who undergo professional training usually find it very difficult to balance tertiary study, work, and family responsibilities. It is important to investigate factors contributing to and consequences resulting from adult students’ academic procrastination, so that we can provide them with targeted help. In this study, we collected data from more than 1800 students who participated in a postgraduate training programme for teaching professionals. Specifically, we examined data on assignment grades, student demographic factors, and assignment extensions. Our analysis suggests that students tend to procrastinate on assessment tasks that are not closely related to their professional practice and skills. We also find that students using personal reasons to apply for extensions are more likely to not complete their assignments, even after being given extensions. With regards to demographic factors, female students, students aged 35 and over, and students working at intermediate and composite schools have a higher tendency to postpone their work. By analyzing the grade means and variances of assignments submitted early, on time, and with extensions, we conclude that procrastination is negatively associated with academic performance

Erosive Rainfall Thresholds Identification Using Statistical Approaches in a Karst Yellow Soil Mountain Erosion-Prone Region in Southwest China

Karst yellow soil is one of the most important cultivated soils in southwest China. At present, only a few studies have dealt with rainfall erosivity and erosive rainfall thresholds in the karst yellow soil region. This paper utilizes statistical methods to identify erosive rainfall thresholds and slope erosion-prone areas in the Qianzhong region. This analysis is based on long-term experimental data from 10 experimental stations and 69 experimental plots within the region in 2006 to 2022. The findings show the following: The rainfall amount threshold was 12.66 mm for woodland plots, 10.57 mm for grassland plots, 9.94 mm for farmland plots, and 8.93 mm for fallow plots. Soil and water conservation measures in forestry and grassland effectively increase the rainfall amount thresholds. Compared to farmland, the rainfall threshold increased by 27.32% for woodland and 6.32% for grassland. Bare land and farmland are erosion-prone areas in the karst yellow soil region. The erosive rainfall thresholds for farmland plots with slopes of 13&deg;, 15&deg;, 20&deg;, 23&deg;, and 25&deg; were 10.41 mm, 10.28 mm, 9.66 mm, 9.52 mm, and 9.15 mm, respectively. With the increase in the 13&ndash;25&deg; slope gradient of farmland, the initial rainfall required for runoff generation leads to a reduction. The wrong selection indices (WSI) of all landcover plots were less than 10%, and the efficiency indices (EFF) were between 80.43% and 90.25%. The relative error index (REI) of the erosive rainfall thresholds for all landcover runoff plots was less than 0.50%, very close to 0, indicating that these thresholds have small errors and high accuracy. This study gained a better understanding of natural rainfall-induced erosion characteristics in the study area, determined rainfall thresholds for distinguishing erosive rainfall events from non-erosive across different landcover types, and reduced the workload of calculating rainfall erosivity while enhancing the accuracy of soil erosion forecasting and simulation in the karst mountain yellow soil area

Comparison of Curative Complications between Mammotome-Assisted Minimally Invasive Resection and Conventional Open Resection for Breast Neoplasm: A Retrospective Clinical Study

Background. To know the clinical value of mammotome-assisted minimally invasive resection (MAMIR) in the treatment of patients with breast neoplasm, we performed a retrospective clinical study for the patients treated with the MAMIR and conventional open resection (COR). Methods. Postoperative complications were compared between 40 patients treated with the MAMIR and 40 patients treated with the COR. The postoperative complications mainly included intraoperative blood loss, hospitalization days, operative time, surgical scar, and incidence of postoperative complications. Results. We found that the amount of intraoperative blood loss, hospitalization days, operative time, surgical scar, and incidence of postoperative complications in the MAMIR group were significantly lower than those of patients in the COR group. Conclusion. Our results indicated that patients with breast neoplasm treated with the MAMIR had better outcomes, which reinforced the advantage of this approach.</jats:p

Proteomic Analysis Revealed Different Molecular Mechanisms of Response to PEG Stress in Drought-Sensitive and Drought-Resistant Sorghums

Drought is the major limiting factor that directly or indirectly inhibits the growth and reduces the productivity of sorghum (Sorghum bicolor (L.) Moench). As the main vegetative organ of sorghum, the response mechanism of the leaf to drought stress at the proteomic level has not been clarified. In the present study, nano-scale liquid chromatography mass spectrometry (nano-LC-MS/MS) technology was used to compare the changes in the protein expression profile of the leaves of drought-sensitive (S4 and S4-1) and drought-resistant (T33 and T14) sorghum varieties at the seedling stage under 25% PEG-6000 treatment for 24 h. A total of 3927 proteins were accurately quantitated and 46, 36, 35, and 102 differentially abundant proteins (DAPs) were obtained in the S4, S4-1, T14, and T33 varieties, respectively. Four proteins were randomly selected for parallel reaction monitoring (PRM) assays, and the results verified the reliability of the mass spectrometry (MS) results. The response mechanism of the drought-sensitive sorghum leaves to drought was attributed to the upregulation of proteins involved in the tyrosine metabolism pathway with defense functions. Drought-resistant sorghum leaves respond to drought by promoting the TCA cycle, enhancing sphingolipid biosynthesis, interfering with triterpenoid metabolite synthesis, and influencing aminoacyl-tRNA biosynthesis. The 17 screened important candidate proteins related to drought stress were verified by quantitative real-time PCR (qRT-PCR), the results of which were consistent with the results of the proteomic analysis. This study lays the foundation for revealing the drought-resistance mechanism of sorghum at the protein level. These findings will help us cultivate and improve new drought-resistant sorghum varieties