Three-dimensional craniofacial changes with maxillary expansion in young adult patients with different craniofacial morphology

Objectives:

Skeletally mature patients with transverse deficiency are best treated with surgically assisted rapid palatal expansion (RPE) procedure. Recent studies have shown that microimplant-assisted RPE (MARPE) appliances can be effective in achieving skeletal expansion in young adults. This retrospective study aimed to evaluate the skeletal and dental alveolar changes in response to treatment with MARPE appliances in three types of anteroposterior skeletal malocclusions using cone-beam computed tomography (CBCT) scans.

Material and Methods:

Seventy-eight subjects diagnosed with maxillary transverse deficiency and treated with the MARPE appliance (mean age of 22.9 ± 4.2 years) were divided into skeletal Class I, II, and III malocclusions with 26 subjects in each group. Pre- and post-treatment CBCT scans were used for superimposition to examine the skeletal and dentoalveolar changes following maxillary expansion treatment.

Results:

Significant lateral separation of the maxilla was found at the levels of the nasal floor, interzygomatic bones, and the inferior palatine margin of the alveolar process (P < 0.05) in the whole sample. Most of the sagittal and vertical variables change significantly in the whole sample and each studied group separately. Intergroup comparisons revealed no significant differences among the three skeletal classes except for the left frontozygomatic angle, left maxillary inclination angle, and torque in the first and second premolars. In Class III patients, the maxilla moved forward significantly in most of the cases (eight of 26 cases) (0.88°, P < 0.05) and the mandible moved downward and backward improving the anteroposterior skeletal relationship. Significant differences were also found in the vertical measurements (N-Me, MMP, and MP/SN, P < 0.05) in all three types of anteroposterior malocclusions.

Conclusion:

Maxillary expansion with the MARPE appliance in young adult patients induced different skeletal and dentoalveolar changes in the anteroposterior and vertical dimensions in each skeletal malocclusion with no significant difference among the three skeletal classes.

Integrative approaches for studying the role of noncoding RNAs in influencing drug efficacy and toxicity

INTRODUCTION

Drug efficacy and toxicity are important factors for evaluation in drug development. Drug metabolizing enzymes and transporters (DMETs) play an essential role in drug efficacy and toxicity. Noncoding RNAs (ncRNAs) have been implicated to influence inter-individual variations in drug efficacy and safety by regulating DMETs. An efficient strategy is urgently needed to identify and functionally characterize ncRNAs that mediate drug efficacy and toxicity through regulating DMETs.

AREAS COVERED

We outline an integrative strategy to identify ncRNAs that modulate DMETs. We include reliable tools and databases for computational prediction of ncRNA targets with regard to their advantages and limitations. Various biochemical, molecular, and cellular assays are discussed for in vitro experimental verification of the regulatory function of ncRNAs. In vivo approaches for association of ncRNAs with drug treatment and toxicity are also reviewed.

EXPERT OPINION

A streamlined integration of computational prediction and wet-lab validation is important to elucidate mechanisms of ncRNAs in the regulation of DMETs related to drug efficacy and safety. Bioinformatic analyses using open-access tools and databases serve as a powerful booster for ncRNA Research in toxicology. Further refinement of computational algorithms and experimental technologies is needed to improve accuracy and efficiency in ncRNA target identification and characterization.

Machine Learning Models for Predicting Cytotoxicity of Nanomaterials

The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. However, the different ways the evaluations were performed, such as the utilized assays, cell lines, and the differences of the produced nanoparticles, make it difficult for scientists to analyze and effectively compare toxicities of nanomaterials. Fortunately, machine learning has emerged as a powerful tool for the prediction of nanotoxicity based on the available data. Among different types of toxicity assessments, nanomaterial cytotoxicity was the focus here because of the high sensitivity of cytotoxicity assessment to different treatments without the need for complicated and time-consuming procedures. In this review, we summarized recent studies that focused on the development of machine learning models for prediction of cytotoxicity of nanomaterials. The goal was to provide insight into predicting potential nanomaterial toxicity and promoting the development of safe nanomaterials.

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample

BACKGROUND

Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data.

RESULTS

In this study, we use the first comprehensive and well-characterized somatic reference data sets from the SEQC2 consortium to investigate best practices for using a deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for a cancer cell line by the consortium, we identify the best strategy for building robust models on multiple data sets derived from samples representing real scenarios, for example, a model trained on a combination of real and spike-in mutations had the highest average performance.

CONCLUSIONS

The strategy identified in our study achieved high robustness across multiple sequencing technologies for fresh and FFPE DNA input, varying tumor/normal purities, and different coverages, with significant superiority over conventional detection approaches in general, as well as in challenging situations such as low coverage, low variant allele frequency, DNA damage, and difficult genomic regions.

Assessing reproducibility of inherited variants detected with short-read whole genome sequencing

BACKGROUND

Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS.

RESULTS

To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when > 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30×.

CONCLUSIONS

Our findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS.

Machine Learning Models for Predicting Liver Toxicity

Liver toxicity is a major adverse drug reaction that accounts for drug failure in clinical trials and withdrawal from the market. Therefore, predicting potential liver toxicity at an early stage in drug discovery is crucial to reduce costs and the potential for drug failure. However, current in vivo animal toxicity testing is very expensive and time consuming. As an alternative approach, various machine learning models have been developed to predict potential liver toxicity in humans. This chapter reviews current advances in the development and application of machine learning models for prediction of potential liver toxicity in humans and discusses possible improvements to liver toxicity prediction.

Structures of Endocrine-Disrupting Chemicals Correlate with the Activation of 12 Classic Nuclear Receptors

Endocrine-disrupting chemicals (EDCs) can inadvertently interact with 12 classic nuclear receptors (NRs) that disrupt the endocrine system and cause adverse effects. There is no widely accepted understanding about what structural features make thousands of EDCs able to activate different NRs as well as how these structural features exert their functions and induce different outcomes at the cellular level. This paper applies the hierarchical characteristic fragment methodology and high-throughput screening molecular docking to comprehensively explore the structural and functional features of EDCs for the 12 NRs based on more than 7000 chemicals from curated datasets. EDCs share three levels of key fragments. The primary and secondary fragments are associated with the binding of EDCs to four groups of receptors: steroidal nuclear receptors (SNRs, including androgen, estrogen, glucocorticoid, mineralocorticoid, and progesterone), retinoic acid receptors, thyroid hormone receptors, and vitamin D receptors. The tertiary fragments determine the activity type by interacting with two key locations in the ligand-binding domains of NRs (N-H5-H3-C and N-H7-H11-C for SNRs and N-H5-H5'-H2'-H3-C and N-H6'-H11-C for non-SNRs). The resulting compiled structural fragments of EDCs together with elucidated compound NR binding modes provide a framework for understanding the interactions between EDCs and NRs, facilitating faster and more accurate screening of EDCs for multiple NRs in the future.

Hidden biases in germline structural variant detection

BACKGROUND

Genomic structural variations (SV) are important determinants of genotypic and phenotypic changes in many organisms. However, the detection of SV from next-generation sequencing data remains challenging.

RESULTS

In this study, DNA from a Chinese family quartet is sequenced at three different sequencing centers in triplicate. A total of 288 derivative data sets are generated utilizing different analysis pipelines and compared to identify sources of analytical variability. Mapping methods provide the major contribution to variability, followed by sequencing centers and replicates. Interestingly, SV supported by only one center or replicate often represent true positives with 47.02% and 45.44% overlapping the long-read SV call set, respectively. This is consistent with an overall higher false negative rate for SV calling in centers and replicates compared to mappers (15.72%). Finally, we observe that the SV calling variability also persists in a genotyping approach, indicating the impact of the underlying sequencing and preparation approaches.

CONCLUSIONS

This study provides the first detailed insights into the sources of variability in SV identification from next-generation sequencing and highlights remaining challenges in SV calling for large cohorts. We further give recommendations on how to reduce SV calling variability and the choice of alignment methodology.

[Characteristics and influencing factors of newly HIV infection among newly confirmed HIV/AIDS cases in Ningbo city, 2017-2020]

Objective: To identify the characteristics and influencing factors of local HIV infection among newly confirmed cases in Ningbo from 2017 to 2020 to provide evidence for traceability investigations on critical cases and facilitate the detection procedures and reduce new HIV infection. Methods: From January 1, 2017, to December 31, 2020, the newly confirmed HIV/AIDS in Ningbo were recruited. An epidemiological questionnaire was used to collect relevant information, including demography, sexual behaviors, results of HIV antibody tests, and the route of HIV transmission. According to the HIV testing, history of risk behaviors, and the level of CD4⁺ lymphocytes after confirmation, the HIV infection was acquired in the previous year, or the place was in Ningbo. The EpiData 3.1 and SPSS 23.0 software were used for input, sorting database and statistical analysis. Results: A total of 2 044 HIV/AIDS on-site investigations were completed. The average age of the subjects was (40.6±15.3) years old, including 1 684 males (82.4%), 758 unmarrieds (37.1%), 1 072 (52.5%) registered as permanent residents in Ningbo, 1 253 (61.3%) with junior high school education or below, 979 (47.9%) lived in Ningbo for more than five years. The proportion of local, new HIV infections was 34.34% (702/2 044). Multivariate logistic analysis showed that the proportion of local newly HIV infection was higher among those who were confirmed in 2020 (compared with the 2017 confirmed cases, OR=1.422, 95%CI:1.092-1.851), whose occupations were students/teachers/cadres/retirees (compared to commercial service/catering/public place service personnel, OR=1.682, 95%CI: 1.307-2.165), meeting sex partners via male social software locally in the last year (compared with without using related dating software, OR=1.353, 95%CI: 1.073-1.706). Conclusions: The proportion of local HIV infection of newly confirmed HIV/AIDS was relatively high in Ningbo city from 2017 to 2020. Meeting gay sex partners through local male social software appeared a risk factor for local newly HIV infection. Traceability investigations and internet intervention should be carried out for MSM. While male social software should be focused on identifying and controlling the risk of local newly HIV infection.

[Conducting analysis on HIV tracing and molecular network for promoting precise detection,prevention and control of AIDS]

The current sexual transmission of newly infected HIV cases is complicated in China. It is crucial for interrupting HIV transmission by HIV tracing and molecular network analysis,conducting early detection and precise prevention among HIV-positive individuals. The articles in this issue focused on the global situation of HIV tracing, and molecular network analysis in promoting accurate detection and prevention of AIDS started from analyzing the reported epidemiological characteristics of HIV/AIDS in Ningbo city from 2017 to 2020. Relations of HIV transmission between HIV positive individuals and sex partners by tracing investigation were also, analyzed. Data on HIV transmission mode by combining social and molecular networks were gathered to provide evidence for applied research on precise detection, prevention, and control of AIDS.

[HIV infection and related factors of traceability efficiency among sex partners of HIV positive men who have sex with men]

Objective: To investigate the situation related to HIV infection and influencing factors of traceability efficiency among sex partners of HIV positive men who have sex with men (MSM). Methods: A cross-sectional survey was conducted to investigate the traceability among sex partners of HIV-positive MSM in Ningbo from 2018 to 2020. Limiting-antigen avidity enzyme immunoassay determined recent HIV infection. The classified data was evaluated by chi-square test, and factors of traceability efficiency were analyzed by multivariate logistic regression. Results: A total of 374 newly confirmed HIV-positive MSM were recruited to participate in the HIV test in Ningbo from 2018 to 2020.HIV positive rate of sex partner was 15.7% (75/479,95%CI:12.4%-18.9%). HIV positive rates of sex partner of recent HIV infection MSM was 31.8% (21/66,95%CI:20.3%-43.4%). The proportion of newly confirmed HIV-positive sex partners of recent HIV infection MSM (76.2%) was higher than that of long-term HIV infection MSM (48.1%). The difference was statistically significant (P=0.028). Results from the multivariate logistic regression analysis showed that HIV traceability efficiency was higher in the following subpopulations as; HIV positive MSM who were 36-45 years old (compared with 18-25 years old, OR=3.973,95%CI:1.364-11.569), HIV active detection (compared with HIV passive detection, OR=1.896, 95%CI:1.083-3.319), recent HIV infection MSM (compared with long-term HIV infection MSM, OR=3.733, 95%CI:1.844-7.556). Conclusions: HIV positive rate among partners of HIV positive MSM was very high. The traceability efficiency, which was recent HIV infection MSM and HIV active detection, was high. It is suggested to strengthen the traceability and focus on the newly confirmed HIV-positive MSM in VCT clinics.

The SEQC2 epigenomics quality control (EpiQC) study

BACKGROUND

Cytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify types of cancer and other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and cross-validated resource for epigenetics research from the FDA's Epigenomics Quality Control Group.

RESULTS

Each sample is processed in multiple replicates by three whole-genome bisulfite sequencing (WGBS) protocols (TruSeq DNA methylation, Accel-NGS MethylSeq, and SPLAT), oxidative bisulfite sequencing (TrueMethyl), enzymatic deamination method (EMSeq), targeted methylation sequencing (Illumina Methyl Capture EPIC), single-molecule long-read nanopore sequencing from Oxford Nanopore Technologies, and 850k Illumina methylation arrays. After rigorous quality assessment and comparison to Illumina EPIC methylation microarrays and testing on a range of algorithms (Bismark, BitmapperBS, bwa-meth, and BitMapperBS), we find overall high concordance between assays, but also differences in efficiency of read mapping, CpG capture, coverage, and platform performance, and variable performance across 26 microarray normalization algorithms.

CONCLUSIONS

The data provided herein can guide the use of these DNA reference materials in epigenomics research, as well as provide best practices for experimental design in future studies. By leveraging seven human cell lines that are designated as publicly available reference materials, these data can be used as a baseline to advance epigenomics research.

Human transthyretin binding affinity of halogenated thiophenols and halogenated phenols: An in vitro and in silico study

Serious harmful effects have been reported for thiophenols, which are widely used industrial materials. To date, little information is available on whether such chemicals can elicit endocrine-related detrimental effects. Herein the potential binding affinity and underlying mechanism of action between human transthyretin (hTTR) and seven halogenated-thiophenols were examined experimentally and computationally. Experimental results indicated that the halogenated-thiophenols, except for pentafluorothiophenol, were powerful hTTR binders. The differentiated hTTR binding affinity of halogenated-thiophenols and halogenated-phenols were observed. The hTTR binding affinity of mono- and di-halo-thiophenols was higher than that of corresponding phenols; while the opposite relationship was observed for tri- and penta-halo-thiophenols and phenols. Our results also confirmed that the binding interactions were influenced by the degree of ligand dissociation. Molecular modeling results implied that the dominant noncovalent interactions in the molecular recognition processes between hTTR and halogenated-thiophenols were ionic pair, hydrogen bonds and hydrophobic interactions. Finally, a model with acceptable predictive ability was developed, which can be used to computationally predict the potential hTTR binding affinity of other halogenated-thiophenols and phenols. Taken together, our results highlighted that more research is needed to determine their potential endocrine-related harmful effects and appropriate management actions should be taken to promote their sustainable use.

Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing

The lack of samples for generating standardized DNA datasets for setting up a sequencing pipeline or benchmarking the performance of different algorithms limits the implementation and uptake of cancer genomics. Here, we describe reference call sets obtained from paired tumor-normal genomic DNA (gDNA) samples derived from a breast cancer cell line-which is highly heterogeneous, with an aneuploid genome, and enriched in somatic alterations-and a matched lymphoblastoid cell line. We partially validated both somatic mutations and germline variants in these call sets via whole-exome sequencing (WES) with different sequencing platforms and targeted sequencing with >2,000-fold coverage, spanning 82% of genomic regions with high confidence. Although the gDNA reference samples are not representative of primary cancer cells from a clinical sample, when setting up a sequencing pipeline, they not only minimize potential biases from technologies, assays and informatics but also provide a unique resource for benchmarking 'tumor-only' or 'matched tumor-normal' analyses.

Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing

Clinical applications of precision oncology require accurate tests that can distinguish true cancer-specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole-genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection.

Machine Learning for Predicting Risk of Drug-Induced Autoimmune Diseases by Structural Alerts and Daily Dose

An effective approach for assessing a drug’s potential to induce autoimmune diseases (ADs) is needed in drug development. Here, we aim to develop a workflow to examine the association between structural alerts and drugs-induced ADs to improve toxicological prescreening tools. Considering reactive metabolite (RM) formation as a well-documented mechanism for drug-induced ADs, we investigated whether the presence of certain RM-related structural alerts was predictive for the risk of drug-induced AD. We constructed a database containing 171 RM-related structural alerts, generated a dataset of 407 AD- and non-AD-associated drugs, and performed statistical analysis. The nitrogen-containing benzene substituent alerts were found to be significantly associated with the risk of drug-induced ADs (odds ratio = 2.95, p = 0.0036). Furthermore, we developed a machine-learning-based predictive model by using daily dose and nitrogen-containing benzene substituent alerts as the top inputs and achieved the predictive performance of area under curve (AUC) of 70%. Additionally, we confirmed the reactivity of the nitrogen-containing benzene substituent aniline and related metabolites using quantum chemistry analysis and explored the underlying mechanisms. These identified structural alerts could be helpful in identifying drug candidates that carry a potential risk of drug-induced ADs to improve their safety profiles.

Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials

Nanomaterials have drawn increasing attention due to their tunable and enhanced physicochemical and biological performance compared to their conventional bulk materials. Owing to the rapid expansion of the nano-industry, large amounts of data regarding the synthesis, physicochemical properties, and bioactivities of nanomaterials have been generated. These data are a great asset to the scientific community. However, the data are on diverse aspects of nanomaterials and in different sources and formats. To help utilize these data, various databases on specific information of nanomaterials such as physicochemical characterization, biomedicine, and nano-safety have been developed and made available online. Understanding the structure, function, and available data in these databases is needed for scientists to select appropriate databases and retrieve specific information for research on nanomaterials. However, to our knowledge, there is no study to systematically compare these databases to facilitate their utilization in the field of nanomaterials. Therefore, we reviewed and compared eight widely used databases of nanomaterials, aiming to provide the nanoscience community with valuable information about the specific content and function of these databases. We also discuss the pros and cons of these databases, thus enabling more efficient and convenient utilization.

Predictive Models to Identify Small Molecule Activators and Inhibitors of Opioid Receptors

Opioid receptors (OPRs) are the main targets for the treatment of pain and related disorders. The opiate compounds that activate these receptors are effective analgesics but their use leads to adverse effects, and they often are highly addictive drugs of abuse. There is an urgent need for alternative chemicals that are analgesics and to reduce/avoid the unwanted effects in order to relieve the public health crisis of opioid addiction. Here, we aim to develop computational models to predict the OPR activity of small molecule compounds based on chemical structures and apply these models to identify novel OPR active compounds. We used four different machine learning algorithms to build models based on quantitative high throughput screening (qHTS) data sets of three OPRs in both agonist and antagonist modes. The best performing models were applied to virtually screen a large collection of compounds. The model predicted active compounds were experimentally validated using the same qHTS assays that generated the training data. Random forest was the best classifier with the highest performance metrics, and the mu OPR (OPRM)-agonist model achieved the best performance measured by AUC-ROC (0.88) and MCC (0.7) values. The model predicted actives resulted in hit rates ranging from 2.3% (delta OPR-agonist) to 15.8% (OPRM-agonist) after experimental confirmation. Compared to the original assay hit rate, all models enriched the hit rate by ≥2-fold. Our approach produced robust OPR prediction models that can be applied to prioritize compounds from large libraries for further experimental validation. The models identified several novel potent compounds as activators/inhibitors of OPRs that were confirmed experimentally. The potent hits were further investigated using molecular docking to find the interactions of the novel ligands in the active site of the corresponding OPR.

Developing QSAR Models with Defined Applicability Domains on PPARγ Binding Affinity Using Large Data Sets and Machine Learning Algorithms

Chemicals may cause adverse effects on human health through binding to peroxisome proliferator-activated receptor γ (PPARγ). Hence, binding affinity is useful for evaluating chemicals with potential endocrine-disrupting effects. Quantitative structure-activity relationship (QSAR) regression models with defined applicability domains (ADs) are important to enable efficient screening of chemicals with PPARγ binding activity. However, lack of large data sets hindered the development of QSAR models. In this study, based on PPARγ binding affinity data sets curated from various sources, 30 QSAR models were developed using molecular fingerprints, two-dimensional descriptors, and five machine learning algorithms. Structure-activity landscapes (SALs) of the training compounds were described by network-like similarity graphs (NSGs). Based on the NSGs, local discontinuity scores were calculated and found to be positively correlated with the cross-validation absolute prediction errors of the models using the different training sets, descriptors, and algorithms. Moreover, innovative ADs were defined based on pairwise similarities between compounds and were found to outperform some conventional ADs. The curated data sets and developed regression models could be useful for evaluating PPARγ-involved adverse effects of chemicals. The SAL analysis and the innovative ADs could facilitate understanding of prediction results from QSAR models.

Risk of hearing loss among multidrug-resistant tuberculosis patients according to cumulative aminoglycoside dose

SETTING: The ototoxic effects of aminoglycosides (AGs) lead to permanent hearing loss, which is one of the devastating consequences of multidrug-resistant tuberculosis (MDR-TB) treatment. As AG ototoxicity is dose-dependent, the impact of a surrogate measure of AG exposure on AG-induced hearing loss warrants close attention for settings with limited therapeutic drug monitoring.OBJECTIVE: To explore the prognostic impact of cumulative AG dose on AG ototoxicity in patients following initiation of AG-containing treatment for MDR-TB.DESIGN: This prospective cohort study was nested within an ongoing cluster-randomized trial of nurse case management intervention across 10 MDR-TB hospitals in South Africa.RESULTS: The adjusted hazard of AG regimen modification due to ototoxicity in the high-dose group (≥75 mg/kg/week) was 1.33 times higher than in the low-dose group (<75 mg/kg/week, 95%CI 1.09-1.64). The adjusted hazard of developing audiometric hearing loss was 1.34 times higher than in the low-dose group (95%CI 1.01-1.77). Pre-existing hearing loss (adjusted hazard ratio [aHR] 1.71, 95%CI 1.29-2.26) and age (aHR 1.16 per 10 years of age, 95%CI 1.01-1.33) were also associated with an increased risk of hearing loss.CONCLUSION: MDR-TB patients with high AG dose, advanced age and pre-existing hearing loss have a significantly higher risk of AG-induced hearing loss. Those at high risk may be candidates for more frequent monitoring or AG-sparing regimens.

Zhuyu Annao decoction promotes angiogenesis in mice with cerebral hemorrhage by inhibiting the activity of PHD3

Some traditional Chinese decoctions, such as Zhuyu Annao, exert favorable therapeutic effects on acute cerebral hemorrhage, hemorrhagic stroke, and other neurological diseases, but the underlying mechanism remains unclear. This study aimed to determine whether Zhuyu Annao decoction (ZYAND) protects the injured brain by promoting angiogenesis following intracerebral hemorrhage (ICH) and elucidate its specific mechanism. The effect of ZYAND on the nervous system of mice after ICH was explored through behavioral experiments, such as the Morris water maze and Rotarod tests, and its effects on oxidative stress were explored by detecting several oxidative stress markers, including malondialdehyde, nitric oxide, glutathione peroxidase, and superoxide dismutase. Real-time quantitative RT-PCR and WB were used to detect the effects of ZYAND on the levels of prolyl hydroxylase domain 3 (PHD3), hypoxia-inducible factor-1α (HIF-1α), and vascular endothelial growth factor (VEGF) in the brain tissues of mice. The effect of ZYAND on the NF-κB signaling pathway was detected using a luciferase reporter gene. A human umbilical cord vascular endothelial cell angiogenesis experiment was performed to determine whether ZYAND promotes angiogenesis. The Morris water maze test and other behavioral experiments verified that ZYAND improved the neurobehavior of mice after ICH. ZYAND activated the PHD3/HIF-1α signaling pathway, inhibiting the oxidative damage caused by ICH. In angiogenesis experiments, it was found that ZYAND promoted VEGF-induced angiogenesis by upregulating the expression of HIF-1α, and NF-κB signaling regulated the expression of HIF-1α by inhibiting PHD3. ZYAND exerts a reparative effect on brain tissue damaged after ICH through the NF-κB/ PHD3/HIF-1α/VEGF signaling axis.

Cross-oncopanel study reveals high sensitivity and accuracy with overall analytical performance depending on genomic regions

BACKGROUND

Targeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing.

RESULTS

All panels demonstrate high sensitivity across targeted high-confidence coding regions and variant types for the variants previously verified to have variant allele frequency (VAF) in the 5-20% range. Sensitivity is reduced by utilizing VAF thresholds due to inherent variability in VAF measurements. Enforcing a VAF threshold for reporting has a positive impact on reducing false positive calls. Importantly, the false positive rate is found to be significantly higher outside the high-confidence coding regions, resulting in lower reproducibility. Thus, region restriction and VAF thresholds lead to low relative technical variability in estimating promising biomarkers and tumor mutational burden.

CONCLUSION

This comprehensive study provides actionable guidelines for oncopanel sequencing and clear evidence that supports a simplified approach to assess the analytical performance of oncopanels. It will facilitate the rapid implementation, validation, and quality control of oncopanels in clinical use.