Evaluation of electroacupuncture as a non-pharmacological therapy for astrocytic structural aberrations and behavioral deficits in a post-ischemic depression model in mice

BackgroundAscending clinical evidence supports that electroacupuncture (EA) is effective in treating post-ischemic depression (PID), but little is known about how it works at the cellular level. Astrocytes are exquisitely sensitive to their extracellular environment, and under stressful conditions, they may experience aberrant structural remodeling that can potentially cause neuroplastic disturbances and contribute to subsequent changes in mood or behavior.ObjectivesThis study aimed to investigate the effect of EA on behavioral deficits associated with PID in mice and verify the hypothesis that astrocytic morphology may be involved in this impact.MethodsWe established a PID animal model induced by transient bilateral common carotid artery occlusion (BCCAO, 20 min) and chronic restraint stress (CRS, 21 days). EA treatment (GV20 + ST36) was performed for 3 weeks, from Monday to Friday each week. Depressive- and anxiety-like behaviors and sociability were evaluated using SPT, FST, EPM, and SIT. Immunohistochemistry combined with Sholl and cell morphological analysis was utilized to assess the process morphology of GFAP+ astrocytes in mood-related regions. The potential relationship between morphological changes in astrocytes and behavioral output was detected by correlation analysis.ResultsBehavioral assays demonstrated that EA treatment induced an overall reduction in behavioral deficits, as measured by the behavioral Z-score. Sholl and morphological analyses revealed that EA prevented the decline in cell complexity of astrocytes in the prefrontal cortex (PFC) and the CA1 region of the hippocampus, where astrocytes displayed evident deramification and atrophy of the branches. Eventually, the correlation analysis showed there was a relationship between behavioral emotionality and morphological changes.ConclusionOur findings imply that EA prevents both behavioral deficits and structural abnormalities in astrocytes in the PID model. The strong correlation between behavioral Z-scores and the observed morphological changes confirms the notion that the weakening of astrocytic processes may play a crucial role in depressive symptoms, and astrocytes could be a potential target of EA in the treatment of PID

MoNET: an R package for multi-omic network analysis

The increasing availability of multi-omic data has enabled the discovery of disease biomarkers in different scales. Understanding the functional interaction between multi-omic biomarkers is becoming increasingly important due to its great potential for providing insights of the underlying molecular mechanism.Leveraging multiple biological network databases, we integrated the relationship between single nucleotide polymorphisms (SNPs), genes/proteins and metabolites, and developed an R package Multi-omic Network Explorer Tool (MoNET) for multi-omic network analysis. This new tool enables users to not only track down the interaction of SNPs/genes with metabolome level, but also trace back for the potential risk variants/regulators given altered genes/metabolites. MoNET is expected to advance our understanding of the multi-omic findings by unveiling their transomic interactions and is likely to generate new hypotheses for further validation.The MoNET package is freely available on https://github.com/JW-Yan/MONET.Supplementary data are available at Bioinformatics online

Association Between Three-Dimensional Transrectal Ultrasound Findings and Tumor Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: An Observational Study

BackgroundThere is a significant demand for the development of non-surgical methods for the evaluation of complete response to tumor therapy. Predicting ability and image quality of routine imaging has not been satisfactory. To avoid the deficiencies, we assessed the capability of three-dimensional transrectal ultrasound in predicting the response to neoadjuvant chemoradiotherapy in rectal cancer patients.MethodsThe inclusion criteria were patients with locally advanced rectal adenocarcinoma, receiving capecitabine-based neoadjuvant chemoradiotherapy, distance from anal verge (≤6 cm), clinical stage T3-4 and/or N+ without evidence of distant metastasis, and restaging ycT0-3a (T3a <5 mm) after the end of neoadjuvant chemoradiotherapy. Three-dimensional transrectal ultrasound was performed 7 weeks after neoadjuvant chemoradiotherapy to discern the patients with complete response from the others. Eight main parameters were obtained from three-dimensional transrectal ultrasound: thickness of muscularis on the residual side, thickness of contralateral muscularis, angle of residual arc, regularity of the shape, integrity of the mucosal layer, blurring of the margin, internal echo, and posterior echo. The association between tumor response and three-dimensional transrectal ultrasound parameters was analyzed, and a model was developed by logistic regression.ResultsBetween 2014 and 2019, 101 patients were recruited; 72 cases received total mesorectal excision, and 29 cases underwent watch-and-wait. Among the three-dimensional transrectal ultrasound parameters, the adjusted-thickness of the muscularis (P<0.01), angle of the residual arc (P<0.01), and regularity of the residual shape (P<0.01) were strongly associated with tumor response. In the dataset with total mesorectal excision cases (TME dataset), the residual adjusted-thickness (odds ratio [OR]=4.88, 95% confidence interval [CI]=1.44–16.6, P=0.01) and regularity of the residual shape (OR=5.00, 95% CI=1.13–22.2, P=0.03) were kept in the final logistic model. The area under the curve of the logistic model was 0.84. Among these parameters, residual adjusted-thickness correlated significantly with tumor response. Additionally, we observed similar results in the whole population of 101 cases (whole dataset) and in the cross-validation.ConclusionThree-dimensional transrectal ultrasound model is a valuable method for predicting tumor response in rectal cancer patients undergoing neoadjuvant chemoradiotherapy, which should be included as a factor for evaluating clinical complete response.Trial RegistrationThis trial was registered with ClinicalTrials.gov, number NCT02605265. Registered 9 November 2015 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/record/NCT0260526

Heparan Sulfate Facilitates Spike Protein-Mediated SARS-CoV-2 Host Cell Invasion and Contributes to Increased Infection of SARS-CoV-2 G614 Mutant and in Lung Cancer

The severe acute respiratory syndrome (SARS)-like coronavirus disease (COVID-19) is caused by SARS-CoV-2 and has been a serious threat to global public health with limited treatment. Cellular heparan sulfate (HS) has been found to bind SARS-CoV-2 spike protein (SV2-S) and co-operate with cell surface receptor angiotensin-converting enzyme 2 (ACE2) to mediate SARS-CoV-2 infection of host cells. In this study, we determined that host cell surface SV2-S binding depends on and correlates with host cell surface HS expression. This binding is required for SARS-Cov-2 virus to infect host cells and can be blocked by heparin lyase, HS antagonist surfen, heparin, and heparin derivatives. The binding of heparin/HS to SV2-S is mainly determined by its overall sulfation with potential, minor contribution of specific SV2-S binding motifs. The higher binding affinity of SV2-S G614 mutant to heparin and upregulated HS expression may be one of the mechanisms underlying the higher infectivity of the SARS-CoV-2 G614 variant and the high vulnerability of lung cancer patients to SARS-CoV-2 infection, respectively. The higher host cell infection by SARS-CoV-2 G614 variant pseudovirus and the increased infection caused by upregulated HS expression both can be effectively blocked by heparin lyase and heparin, and possibly surfen and heparin derivatives too. Our findings support blocking HS-SV2-S interaction may provide one addition to achieve effective prevention and/treatment of COVID-19

Monitoring response to neoadjuvant therapy for breast cancer in all treatment phases using an ultrasound deep learning model

PurposeThe aim of this study was to investigate the value of a deep learning model (DLM) based on breast tumor ultrasound image segmentation in predicting pathological response to neoadjuvant chemotherapy (NAC) in breast cancer.MethodsThe dataset contains a total of 1393 ultrasound images of 913 patients from Renmin Hospital of Wuhan University, of which 956 ultrasound images of 856 patients were used as the training set, and 437 ultrasound images of 57 patients underwent NAC were used as the test set. A U-Net-based end-to-end DLM was developed for automatically tumor segmentation and area calculation. The predictive abilities of the DLM, manual segmentation model (MSM), and two traditional ultrasound measurement methods (longest axis model [LAM] and dual-axis model [DAM]) for pathological complete response (pCR) were compared using changes in tumor size ratios to develop receiver operating characteristic curves.ResultsThe average intersection over union value of the DLM was 0.856. The early-stage ultrasound-predicted area under curve (AUC) values of pCR were not significantly different from those of the intermediate and late stages (p< 0.05). The AUCs for MSM, DLM, LAM and DAM were 0.840, 0.756, 0.778 and 0.796, respectively. There was no significant difference in AUC values of the predictive ability of the four models.ConclusionUltrasonography was predictive of pCR in the early stages of NAC. DLM have a similar predictive value to conventional ultrasound for pCR, with an add benefit in effectively improving workflow

Genome-wide association and interaction studies of CSF T-tau/Aβ42 ratio in ADNI cohort

The pathogenic relevance in Alzheimer’s disease (AD) presents a decrease of cerebrospinal fluid (CSF) amyloid-ß42 (Aß42) burden and an increase in CSF total-tau (T-tau) levels. In this work, we performed genome-wide association study (GWAS) and genome-wide interaction study (GWIS) of T-tau/Aß42 ratio as an AD imaging quantitative trait (QT) on 843 subjects and 563,980 single nucleotide polymorphisms (SNPs) in ADNI cohort. We aim to identify not only SNPs with significant main effects but also SNPs with interaction effects to help explain “missing heritability”. Linear regression method was used to detect SNP-SNP interactions among SNPs with uncorrected p-value≤0.01 from the GWAS. Age, gender and diagnosis were considered as covariates in both studies. The GWAS results replicated the previously reported AD-related genes APOE, APOC1 and TOMM40, as well as identified 14 novel genes, which showed genome-wide statistical significance. GWIS revealed 7 pairs of SNPs meeting the cell-size criteria and with bonferroni-corrected p-value≤0.05. As we expect, these interaction pairs all had marginal main effects but explained a relatively high-level variance of T-tau/Aß42, demonstrating their potential association with AD pathology

Adaptive restraint design for a diverse population through machine learning

ObjectiveUsing population-based simulations and machine-learning algorithms to develop an adaptive restraint system that accounts for occupant anthropometry variations to further enhance safety balance throughout the whole population.MethodsTwo thousand MADYMO full frontal impact crash simulations at 35 mph using two validated vehicle/restraint models representing a sedan and an SUV along with a parametric occupant model were conducted based on the maximal projection design of experiments, which considers varying occupant covariates (sex, stature, and body mass index) and vehicle restraint design variables (three for airbag, three for safety belt, and one for knee bolster). A Gaussian-process-based surrogate model was trained to rapidly predict occupant injury risks and the associated uncertainties. An optimization framework was formulated to seek the optimal adaptive restraint design policy that minimizes the population injury risk across a wide range of occupant sizes and shapes while maintaining a low difference in injury risks among different occupant subgroups. The effectiveness of the proposed method was tested by comparing the population-wise injury risks under the adaptive design policy and the traditional state-of-the-art design.ResultsCompared to the traditional state-of-the-art design for midsize males, the optimal design policy shows the potential to further reduce the joint injury risk (combining head, chest, and lower extremity injury risks) among the whole population in the sedan and SUV models. Specifically, the two subgroups of vulnerable occupants including tall obese males and short obese females had higher reductions in injury risks.ConclusionsThis study lays out a method to adaptively adjust vehicle restraint systems to improve safety balance. This is the first study where population-based crash simulations and machine-learning methods are used to optimize adaptive restraint designs for a diverse population. Nevertheless, this study shows the high injury risks associated with obese and female occupants, which can be mitigated via restraint adaptability

PCR Techniques and Their Clinical Applications

Kary B. Mullis developed a revolutionary method name polymerase chain reaction (PCR) in 1983, which can synthesize new strand of DNA complementary to the template strand of DNA and produce billions of copies of a DNA fragment only in few hours. Denaturation, annealing, and extension are the three primary steps involved in the PCR process, which generally requires thermocyclers, DNA template, a pair of primers, Taq polymerase, nucleotides, buffers, etc. With the development of PCR, from traditional PCR, quantitative PCR, to next digital PCR, PCR has become a powerful tool in life sciences and medicine. Applications of PCR techniques for infectious diseases include specific or broad-spectrum pathogen detection, assessment and surveillance of emerging infections, early detection of biological threat agents, and antimicrobial resistance analysis. Applications of PCR techniques for genetic diseases include prenatal diagnosis and screening of neonatal genetic diseases. Applications of PCR techniques for cancer research include tumor-related gene detection. This chapter aimed to discuss about the different types of PCR techniques, including traditional PCR, quantitative PCR, digital PCR, etc., and their applications for rapid detection, mutation screen or diagnosis in infectious diseases, inherited diseases, cancer, and other diseases