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Objective Many methods of acetabular reconstruction with total hip arthroplasty (THA) for Crowe type II and III adult developmental dysplasia of the hip (DDH) acetabular bone defect have been implemented clinically. However, there was no study comparing the results of integrated acetabular prosthesis (IAP) with bone grafting (BG). This study aims to investigate the efficacy of IAP and BG for acetabular reconstruction in Crowe type II and III DDH. Methods The clinical data of 45 patients with unilateral Crowe type II and III DDH who underwent THA from January 2020 to January 2023 were retrospectively analyzed. The patients were divided into two groups: 25 patients using 3D‐printed IAP (IAP group) and 20 patients using BG (BG group). The operation time and intraoperative blood loss were recorded. The clinical outcomes were assessed by Harris Hip Score (HHS) and full weight‐bearing time. The radiological outcomes were evaluated by the radiological examination. Accordingly, intraoperative and postoperative complications were observed as well. The data between the two groups were compared by independent sample t‐tests and the Mann–Whitney U rank sum test. Results There were no significant differences between the two groups in Harris Hip Score (HHS) (preoperative, 6 months postoperative, and the last follow‐up), leg length discrepancy (LLD), cup inclination, cup anteversion, vertical center of rotation (V‐COR), horizontal center of rotation (H‐COR) (p > 0.05). The mean HHS in the IAP group was higher than in the BG group at 1 and 3 months postoperative (p

Abstract Background Respiratory Syncytial Virus (RSV) disease in young children ranges from mild cold symptoms to severe symptoms that require hospitalization and sometimes result in death. Studies have shown a statistical association between RSV subtype or phylogenic lineage and RSV disease severity, although these results have been inconsistent. Associations between variation within RSV gene coding regions or residues and RSV disease severity has been largely unexplored. Methods Nasal swabs from children (

Introduction Transcutaneous laser-assisted drug delivery (LADD) is recognized as a developing therapy for skin disorders. Method Current literature was reviewed to summarize current applications for LADD. Discussion 12 clinical applications for this therapy are currently reported. Conclusion LADD has potential for wide application in skin disorder treatment. Lay Summary Laser assisted drug delivery improves drug bioavailability for treatment of skin disorders. This technique is being assessed clinically in disorders ranging from skin cancers to alopecia.

Objective The treatment of acetabular defects is one of the most difficult challenges of revision of total hip arthroplasty (RTHA), and tantalum is regarded as a promising bone substitute material. This study aims to investigate the effectiveness of 3D printed acetabular augment used in RTHA for the treatment of acetabular bone defect. Methods A retrospective analysis of the clinical data of seven patients who had undergone RTHA was carried out using 3D printed acetabular augment from January 2017 to December 2018. The CT data of the patients were exported to Mimics 21.0 software (Materialise, Leuven, Belgium), and the acetabular bone defect augment were designed, printed and then implanted during operation. The postoperative Harris score, visual analogue scale (VAS) score and prosthesis position were observed to evaluate the clinical outcome. A I‐test was used for preoperative and postoperative comparison of the paired‐design dataset. Results A firm attachment of the bone augment to the acetabulum during operation without any complications was found during the follow‐up time 2.8–4.3 years. The VAS score of all patients was found 6.9 ± 1.4 before operation and was 0.7 ± 0.7 at the last follow‐up (P ≤ 0.001), and the Harris hip scores, were 31.9 ± 10.3 and 73.3 ± 12.8 before operation, and at the last follow‐up (P ≤ 0.001), respectively. Moreover, no loosening sign between the bone defect augment and the acetabulum was observed during the entire implantation period. Conclusion 3D printed acetabular augment is effective in reconstructing the acetabulum following an acetabular bone defect revision, which enhances the hip joint function and eventually makes a satisfactory stable prosthetic.

IntroductionDue to advances in combined anti-retroviral treatment (cART), there is an increased burden of age-related cerebrovascular disease (CBVD), in people living with HIV (PWH). The underlying CNS injury can be assessed by measuring cerebral blood flow (CBF) and cerebrovascular reactivity (CVR).Methods35 treatment-naïve PWH and 53 HIV negative controls (HC) were enrolled in this study. Study participants underwent T1-weighted anatomical, pseudo-continuous arterial spin labeling, and resting-state functional MRI to obtain measures of CBF and CVR prior to starting cART treatment and at two-time points (12 weeks and 2 years) post-cART initiation. Controls were scanned at the baseline and 2-year visits. We also measured plasma levels of microparticles of endothelial and glial origin and well-known endothelial inflammation markers, ICAM-1 and VCAM-1, to assess HIV-associated endothelial inflammation and the interaction of these peripheral markers with brain neurovascular function.ResultsHIV infection was found to be associated with reduced CVR and increased levels of endothelial and glial microparticles (MPs) prior to initiation of cART. Further, CVR correlated negatively with peripheral MP levels in PWH.DiscussionOur results suggest that while cART treatment has a beneficial effect on the neurovascular function after initiation, these benefits are suboptimal over time.

Abstract Objective To explore the clinical effect of a new type of bidirectional pressurized porous tantalum screw (PTS) internal fixation in treating femoral neck fractures (FNFs). Methods In this study, geometric models of FNF were first established via reverse engineering method, followed by stimulation of the strength of PTSs in fixation of FNFs. A randomized control trial study was then conducted of 41 patients with FNF from October 2015 to December 2018. These patients included 12 males and 29 females with an average age of 59.9. The 41 patients were randomly divided into two groups: cannulated compression screws (CCSs) group (n = 21) and PTSs group (n = 20). Treatment outcomes in patients were evaluated using multiple imaging techniques, including X‐ray and digital subtraction angiography scanning as well as functional recovery Harris hip score. Without other postoperative complications, the primary outcome was defined as fracture healing after FNF internal fixation. Secondary outcomes are the incidence of the avascular necrosis of femoral head (ANFH), fracture nonunion, and reoperation rate. Results Following PTS internal fixation of FNF, finite element results revealed a firmly fixed fracture with a slight displacement of less than 0.5 mm. At follow‐up, we found a statistically significant difference in Harris scores in the two groups at 1 month and 3 months post‐surgery. In the PTSs group, there was no case of ANFH and fracture nonunion, and the average healing time was 94.45 ± 6.47 days. In the CCSs group, there were four cases of ANFH, the necrosis rate was 19.05% (4/21). There was one case of fracture nonunion in the CCSs group, the nonunion fracture rate was 4.76% (1/21), and the average healing time was 122.54 ± 11.37 days. Five patients underwent total hip arthroplasty, and the reoperation rate was 23.81% (5/21). There were significant differences in the postoperative complications, fracture healing time, and reoperation rate between the two groups (p

BackgroundThis meta-analysis was dedicated to evaluating the safety and effectiveness of indocyanine green (ICG) -mediated fluorescence molecular imaging (FMI) technology in liver tumors resection.MethodsA literature search of PubMed, Embase databases, Cochrane Library, and Web of Science was performed to identify all clinical controlled studies exploring the effects of fluorescence imaging on liver tumors resection. Quality assessment and data extraction of studies were conducted independently by 3 reviewers. Mean difference (MD) and odds ratio (OR) with 95% confidence interval (CI) were calculated using a fixed-effects or random-effects model. The meta-analysis was performed with RevMan 5.3 software.Results14 retrospective cohort studies (RCSs) involving a total of 1227 patients were finally included. The results showed that Fluorescence-assisted liver tumors resection could improve the R0 resection rate (OR = 2.63; 95% CI: 1.46~4.73, p = 0.001), reduce overall complications (OR = 0.66; 95% CI: 0.44~0.97, p = 0.04), biliary fistula (OR = 0.20; 95% CI: 0.05~0.77, p = 0.02), intraoperative blood loss (MD = −70.76, 95% CI: −106.11 to −35.41; p < 0.0001), and shortens hospital stay (MD = −1.41, 95% CI: −1.90 to −0.92; p < 0.00001). There were no significant differences in the incidences of operative time (MD = −8.68, 95% CI: −18.59 to −1.22; p = 0.09), complications of grade III or above (OR = 0.73; 95% CI: 0.43~1.25, p = 0.26), liver failure (OR = 0.86; 95% CI: 0.39~1.89, p = 0.71), and blood transfusion (OR = 0.66; 95% CI: 0.42~1.03, p = 0.07).ConclusionCurrent evidence suggests that ICG-mediated FMI technology could enhance the clinical effectiveness of patients with liver tumors resection and is clinically worthy of promotion.Systematic review registrationPROSPERO, identifier CRD42022368387.

With the development of material science, hydrogels with antibacterial and wound healing properties are becoming common. However, injectable hydrogels with simple synthetic methods, low cost, inherent antibacterial properties, and inherent promoting fibroblast growth are rare. In this paper, a novel injectable hydrogel wound dressing based on carboxymethyl chitosan (CMCS) and polyethylenimine (PEI) was discovered and constructed. Since CMCS is rich in -OH and -COOH and PEI is rich in -NH2, the two can interact through strong hydrogen bonds, and it is theoretically feasible to form a gel. By changing their ratio, a series of hydrogels can be obtained by stirring and mixing with 5 wt% CMCS aqueous solution and 5 wt% PEI aqueous solution at volume ratios of 7:3, 5:5, and 3:7. Characterized by morphology, swelling rate, adhesion, rheological properties, antibacterial properties, in vitro biocompatibility, and in vivo animal experiments, the hydrogel has good injectability, biocompatibility, antibacterial (Staphylococcus aureus: 56.7 × 107 CFU/mL in the blank group and 2.5 × 107 CFU/mL in the 5/5 CPH group; Escherichia coli: 66.0 × 107 CFU/mL in the blank group and 8.5 × 107 CFU/mL in the 5/5 CPH group), and certain adhesion (0.71 kPa in the 5/5 CPH group) properties which can promote wound healing (wound healing reached 98.02% within 14 days in the 5/5 CPH group) and repair of cells with broad application prospects.

Abstract Background Clinical angiography and vascular microperfusion confirmed that the femoral head retains blood supply after a collum femur fracture. However, no animal model accurately mimics this clinical situation. This study was performed to establish a rat model with retained viability of the femoral head and partial vasculature deprivation-induced traumatic caput femoris necrosis by surgery. Methods Thirty rats were randomly divided into three groups (n = 10 per group): normal group, sham-operated group (Control), and ischemic osteonecrosis group. The femoral head of the normal group of rats underwent a gross anatomy study and microangiography to identify femoral head blood supply. Microsurgical techniques were used to cauterize the anterior-superior retinacular vessels to induce osteonecrosis. Hematoxylin and Eosin (H&E) staining were used for femoral head histologic assessment. Morphologic assessments of the deformity in and trabecular bone parameters of the femoral head epiphysis were performed using micro-CT. Results The blood supply of the femoral head in rats primarily came from the anterior-superior, inferior, and posterior retinacular arteries. However, anterior-superior retinacular vasculature deprivation alone was sufficient in inducing femoral head osteonecrosis. H&E showed bone cell loss in nuclear staining, disorganized marrow, and trabecular structure. The bone volume (BV) decreased by 13% and 22% in the ischemic group after 5 and 10 weeks, respectively. The mean trabecular thickness (Tb.Th) decreased from 0.09 to 0.06 mm after 10 weeks. The trabecular spacing (Tb.Sp) increased from 0.03 to 0.05 mm after 5 weeks, and the epiphyseal height-to-diameter (H/D) ratio decreased. Conclusions We developed an original and highly selective rat model that embodied femoral head traumatic osteonecrosis induced by surgical anterior-superior retinacular vasculature deprivation.

An elastoplastic numerical model for calculating the consolidation settlement of wide embankment on soft ground is established using PLAXIS finite element software to investigate the settlement behaviour of soft ground under the wide embankment. The distribution rules are analysed and compared to narrow embankments, such as surface settlements of ground and embankment, lateral displacement of soft ground at the foot of embankment slope and excess pore pressure in soft ground. The influence rule of elastic modulus of soft ground on the settlement of soft ground under wide embankment is discussed. The results show that the settlement distributions of wide and narrow embankments on soft ground are “W” and “V” shapes, respectively. The maximum settlement of wide embankment is near the foot of the embankment slope, which is unequal to the settlement at the centreline of the embankment. The lateral displacement distribution rules of soft ground are both “belly” shaped at the foot of two types of embankments slope. However, the lateral displacement of the wide embankment is larger in each corresponding stage. During the construction period, the excess pore pressure in the soft ground under the wide embankment is much higher than that of the narrow embankment, so the post-construction consolidation time of the wide embankment is longer. Moreover, the macroscopic settlement rule of the wide embankment is still the same with the increase of elastic modulus of soft ground.

Abstract Initiation of combination antiretroviral therapy (cART) reduces inflammation in HIV-infected (HIV+) individuals. Recent studies demonstrated that diffusion MRI based extracellular free water (FW) modeling can be sensitive to neuroinflammation. Here, we investigate the FW in HIV-infection, its temporal evolution, and its association with blood markers, and cognitive scores. Using 96 age-matched participants, we found that FW was significantly elevated in grey and white matter in cART-naïve HIV+ compared to HIV-uninfected (HIV−) individuals at baseline. These increased FW values positively correlated with neurofilament light chain (NfL) and negatively correlated with CD4 counts. FW in grey and white matter, as well as NfL decreased in the HIV+ after 12 weeks of cART treatment. No significant FW differences were noted between the HIV+ and HIV− cohorts at 1 and 2-year follow-up. Results suggest that FW elevation in cART-naïve HIV+ participants is likely due to neuroinflammation. The correlation between FW and NfL, and the improvement in both FW and NfL after 12 weeks of cART treatment further reinforces this conclusion. The longer follow-up at 1 and 2 years suggests that cART helped control neuroinflammation as inferred by FW. Therefore, FW could be used as a biomarker to monitor HIV-associated neuroinflammation.

Abstract Background A substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness. Method We defined airway gene expression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1–10 of illness) and convalescence stage (day 28 of illness). All subjects were assigned a clinical illness severity score (GRSS). Using AIC-based model selection, we built a sparse linear correlate of GRSS based on 41 genes (NGSS1). We also built an alternate model based upon 13 genes associated with severe infection acutely but displaying stable expression over time (NGSS2). Results NGSS1 is strongly correlated with the disease severity, demonstrating a naïve correlation (ρ) of ρ = 0.935 and cross-validated correlation of 0.813. As a binary classifier (mild versus severe), NGSS1 correctly classifies disease severity in 89.6% of the subjects following cross-validation. NGSS2 has slightly less, but comparable, accuracy with a cross-validated correlation of 0.741 and classification accuracy of 84.0%. Conclusion Airway gene expression patterns, obtained following a minimally-invasive procedure, have potential utility for development of clinically useful biomarkers that correlate with disease severity in primary RSV infection.

BackgroundAlthough research indicates an association between hypertensive disorders of pregnancy (HDP) and congenital heart defects (CHDs) in offspring, consistency is still lacking. Therefore, we aimed to synthesize the updated published epidemiologic evidence to estimate the association of maternal HDP with the risk of total CHDs and its phenotypes in offspring.MethodsA systematic search of Web of Science Database, PubMed, and Embase were searched from inception through April 30, 2021 based on a preprepared protocol, and the reference lists were also manually searched. The combined risk estimates were calculated using either the fixed-effect models or random-effect models. Possible heterogeneity moderators were detected by subgroup, sensitivity analyses, and Galbraith plot.ResultsTwenty-four studies involving 477,839 CHDs cases among 40,394,699 participants were included in our meta-analysis. Mothers who had HDP exposure were significantly associated with an increased risk of total CHDs compared with non-exposure. When maternal HDP exposure was further subdivided into pre-eclampsia (OR = 1.79, 95% CI: 1.50–2.13), gestational hypertension (OR = 1.16, 95% CI: 1.02–1.31), and chronic hypertension (OR = 1.68, 95% CI: 1.49–1.89), a significantly increased risk of total CHDs were still presented. Furthermore, a statistically significant increased association was found between maternal HDP exposure and most CHD phenotypes. Besides, relevant heterogeneity moderators have been identified by subgroup and sensitivity analyses.ConclusionOur study suggested that maternal HDP exposure may be associated with an increase in the risk of CHDs in offspring. These findings highlight the need for greater surveillance of pregnant women with HDP exposure to allow early prevention that may be good for reducing the risk of CHDs in offspring.Clinical Trial Registration[www.ClinicalTrials.gov], identifier [CRD42021268093].

Abstract Repetitive head impacts (RHI) are a growing concern due to their possible neurocognitive effects, with research showing a season of RHI produce white matter (WM) changes seen on neuroimaging. We conducted a secondary analysis of diffusion tensor imaging (DTI) data for 28 contact athletes to compare WM changes. We collected pre-season and post-season DTI scans for each subject, approximately 3 months apart. We collected helmet data for the athletes, which we correlated with DTI data. We adapted the SPatial REgression Analysis of DTI (SPREAD) algorithm to conduct subject-specific longitudinal DTI analysis, and developed global inferential tools using functional norms and a novel robust p value combination test. At the individual level, most detected injured regions (93.3%) were associated with decreased FA values. Using meta-analysis techniques to combine injured regions across subjects, we found the combined injured region at the group level occupied the entire WM skeleton, suggesting the WM damage location is subject-specific. Several subject-specific functional summaries of SPREAD-detected WM change, e.g., the $${L}^{\infty }$$ L ∞ norm, significantly correlated with helmet impact measures, e.g. cumulative unweighted rotational acceleration (adjusted p = 0.0049), time between hits rotational acceleration (adjusted p value 0.0101), and time until DTI rotational acceleration (adjusted p = 0.0084), suggesting RHIs lead to WM changes.

Respiratory syncytial virus (RSV) infection results in millions of hospitalizations and thousands of deaths each year. Variations in the adaptive and innate immune response appear to be associated with RSV severity. To investigate the host response to RSV infection in infants, we performed a systems-level study of RSV pathophysiology, incorporating high-throughput measurements of the peripheral innate and adaptive immune systems and the airway epithelium and microbiota. We implemented a novel multi-omic data integration method based on multilayered principal component analysis, penalized regression, and feature weight back-propagation, which enabled us to identify cellular pathways associated with RSV severity. In both airway and immune cells, we found an association between RSV severity and activation of pathways controlling Th17 and acute phase response signaling, as well as inhibition of B cell receptor signaling. Dysregulation of both the humoral and mucosal response to RSV may play a critical role in determining illness severity.

Background: White matter (WM) damage is a consistent finding in HIV-infected (HIV+) individuals. Previous studies have evaluated WM fiber tract-specific brain regions in HIV-associated neurocognitive disorders (HAND) using diffusion tensor imaging (DTI). However, DTI might lack an accurate biological interpretation, and the technique suffers from several limitations. Fixel-based analysis (FBA) and free water corrected DTI (fwcDTI) have recently emerged as useful techniques to quantify abnormalities in WM. Here, we sought to evaluate FBA and fwcDTI metrics between HIV+ and healthy controls (HIV−) individuals. Using machine learning classifiers, we compared the specificity of both FBA and fwcDTI metrics in their ability to distinguish between individuals with and without cognitive impairment in HIV+ individuals.Methods: Forty-two HIV+ and 52 HIV– participants underwent MRI exam, clinical, and neuropsychological assessments. FBA metrics included fiber density (FD), fiber bundle cross section (FC), and fiber density and cross section (FDC). We also obtained fwcDTI metrics such as fractional anisotropy (FAT) and mean diffusivity (MDT). Tract-based spatial statistics (TBSS) was performed on FAT and MDT. We evaluated the correlations between MRI metrics with cognitive performance and blood markers, such as neurofilament light chain (NfL), and Tau protein. Four different binary classifiers were used to show the specificity of the MRI metrics for classifying cognitive impairment in HIV+ individuals.Results: Whole-brain FBA showed significant reductions (up to 15%) in various fiber bundles, specifically the cerebral peduncle, posterior limb of internal capsule, middle cerebellar peduncle, and superior corona radiata. TBSS of fwcDTI metrics revealed decreased FAT in HIV+ individuals compared to HIV– individuals in areas consistent with those observed in FBA, but these were not significant. Machine learning classifiers were consistently better able to distinguish between cognitively normal patients and those with cognitive impairment when using fixel-based metrics as input features as compared to fwcDTI metrics.Conclusion: Our findings lend support that FBA may serve as a potential in vivo biomarker for evaluating and monitoring axonal degeneration in HIV+ patients at risk for neurocognitive impairment.

Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complex correlation structures. BNs have wide applications in many disciplines, including biology, social science, finance and biomedical science. Despite extensive studies in the past, network structure learning from data is still a challenging open question in BN research. In this study, we present a sequential Monte Carlo (SMC)-based three-stage approach, GRowth-based Approach with Staged Pruning (GRASP). A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the quality and diversity of sampled networks which were further improved by a third stage to reclaim edges missed in the skeleton discovery step. GRASP gave very satisfactory results when tested on benchmark networks. Finally, BN structure learning using multiple types of genomics data illustrates GRASP’s potential in discovering novel biological relationships in integrative genomic studies.

Abstract Background For many practical hypothesis testing (H-T) applications, the data are correlated and/or with heterogeneous variance structure. The regression t-test for weighted linear mixed-effects regression (LMER) is a legitimate choice because it accounts for complex covariance structure; however, high computational costs and occasional convergence issues make it impractical for analyzing high-throughput data. In this paper, we propose computationally efficient parametric and semiparametric tests based on a set of specialized matrix techniques dubbed as the PB-transformation. The PB-transformation has two advantages: 1. The PB-transformed data will have a scalar variance-covariance matrix. 2. The original H-T problem will be reduced to an equivalent one-sample H-T problem. The transformed problem can then be approached by either the one-sample Student’s t-test or Wilcoxon signed rank test. Results In simulation studies, the proposed methods outperform commonly used alternative methods under both normal and double exponential distributions. In particular, the PB-transformed t-test produces notably better results than the weighted LMER test, especially in the high correlation case, using only a small fraction of computational cost (3 versus 933 s). We apply these two methods to a set of RNA-seq gene expression data collected in a breast cancer study. Pathway analyses show that the PB-transformed t-test reveals more biologically relevant findings in relation to breast cancer than the weighted LMER test. Conclusions As fast and numerically stable replacements for the weighted LMER test, the PB-transformed tests are especially suitable for “messy” high-throughput data that include both independent and matched/repeated samples. By using our method, the practitioners no longer have to choose between using partial data (applying paired tests to only the matched samples) or ignoring the correlation in the data (applying two sample tests to data with some correlated samples). Our method is implemented as an R package ‘PBtest’ and is available at https://github.com/yunzhang813/PBtest-R-Package.

Purpose Extensive research suggests that maternal prenatal distress is reliably related to perinatal and child health outcomes—which may persist into adulthood. However, basic questions remain regarding mechanisms involved. To better understand these mechanisms, we developed the Understanding Pregnancy Signals and Infant Development (UPSIDE) cohort study, which has several distinguishing features, including repeated assessments across trimesters, analysis of multiple biological pathways of interest, and incorporation of placental structure and function as mediators of child health outcomes.Participants Women with normal risk pregnancies were recruited at

The aim of this study was to quantify, via Magnetic Resonance Spectroscopy (MRS), the effect of combination antiretroviral therapy (cART) on brain metabolites and characterize any possible associations between changes in metabolites, age, blood biomarkers of neuronal damage, functional connectivity and cognitive performance. As cART has dramatically increased the life expectancy of HIV-infected (HIV + ) individuals and unmasked an increase in HIV-associated neurocognitive disorders, it is still not clear whether cART neurotoxicity contributes to these disorders. We hypothesized a bimodal effect, with early cART treatment of HIV infection decreasing inflammation as measured by MRS metabolites and improving cognitive performance, and chronic exposure to cART contributing to persistence of cognitive impairment via its effect on mitochondrial function. Basal ganglia metabolites, functional connectivity, cognitive scores, as well as plasma levels of neurofilament light chain (NfL) and tau protein were measured before and after 12 weeks, 1 year and 2 years of cART in a cohort of 50 cART-naïve HIV + subjects and 72 age matched HIV- healthy controls. Glutamate (Glu) levels were lower in the cART naïve patients than in healthy controls and were inversely correlated with plasma levels of NfL. There were no other significant metabolite differences between HIV + and uninfected individuals. Treatment improved Glu levels in HIV+, however, no associations were found between Glu, functional connectivity and cognitive performance. Stable brain metabolites and plasma levels of NfL and Tau over two-years of follow-ups suggest there are no signs of cART neurotoxicity in this relatively young cohort of HIV + individuals.

The aim of this study was to assess, in the context of cerebral small vessel disease (CSVD), whether cardiovascular risk factors and white matter hyperintensities (WMHs) were associated with brain tissue susceptibility as measured by quantitative susceptibility mapping (QSM). Given that CSVD is diagnosed by the presence of lacunar strokes, periventricular and deep WMHs, increased perivascular spaces, and microbleeds, we expected that QSM could capture changes in brain tissue due to underlying CSVD pathology. We compared a cohort of 101 HIV-infected individuals (mean age ± SD = 53.2 ± 10.9 years) with mild to moderate cardiovascular risk scores, as measured by the Reynolds risk score, to 102 age-matched controls (mean age (SD) = 50.3 (15.7) years) with similar Reynolds scores. We performed brain MRI to assess CSVD burden by acquiring 3D T1-MPRAGE, 3D FLAIR, 2D T2-TSE, and mGRE for QSM. We found that signs of CSVD are significantly higher in individuals with HIV-infection compared to controls and that WMH volumes are significantly correlated with age and cardiovascular risk scores. Regional QSM was associated with cardiovascular risk factors, age, sex, and WMH volumes but not HIV status. These results suggest that QSM may be an early imaging marker reflective of alterations in brain microcirculation.

Rationale: We provide an in-depth description of a comprehensive clinical, immunological, and neuroimaging study that includes a full image processing pipeline. This approach, although implemented in HIV infected individuals, can be used in the general population to assess cerebrovascular health.Aims: In this longitudinal study, we seek to determine the effects of neuroinflammation due to HIV-1 infection on the pathomechanisms of cerebral small vessel disease (CSVD). The study focuses on the interaction of activated platelets, pro-inflammatory monocytes and endothelial cells and their impact on the neurovascular unit. The effects on the neurovascular unit are evaluated by a novel combination of imaging biomarkers.Sample Size: We will enroll 110 HIV-infected individuals on stable combination anti-retroviral therapy for at least three months and an equal number of age-matched controls. We anticipate a drop-out rate of 20%.Methods and Design: Subjects are followed for three years and evaluated by flow cytometric analysis of whole blood (to measure platelet activation, platelet monocyte complexes, and markers of monocyte activation), neuropsychological testing, and brain MRI at the baseline, 18- and 36-month time points. MRI imaging follows the recommended clinical small vessel imaging standards and adds several advanced sequences to obtain quantitative assessments of brain tissues including white matter microstructure, tissue susceptibility, and blood perfusion.Discussion: The study provides further understanding of the underlying mechanisms of CSVD in chronic inflammatory disorders such as HIV infection. The longitudinal study design and comprehensive approach allows the investigation of quantitative changes in imaging metrics and their impact on cognitive performance.

Respiratory syncytial virus (RSV) is the most important cause of respiratory tract illness especially in young infants that develop severe disease requiring hospitalization, and accounting for 74,000–126,000 admissions in the United States (Rezaee et al., 2017; Resch, 2017). Observations of neonatal and infant T cells suggest that they may express different immune markers compared to T-cells from older children. Flow cytometry analysis of cellular responses using “conventional” anti-viral markers (IL2, IFN-γ, TNF, IL10 and IL4) upon RSV-peptide stimulation detected an overall low RSV response in peripheral blood. Therefore we sought an unbiased approach to identify RSV-specific immune markers using RNA-sequencing upon stimulation of infant PBMCs with overlapping peptides representing RSV antigens. To understand the cellular response using transcriptional signatures, transcription factors and cell-type specific signatures were used to investigate breadth of response across peptides. Unexpected from the ICS data, M peptide induced a response equivalent to the F-peptide and was characterized by activation of GATA2, 3, STAT3 and IRF1. This along with upregulation of several unconventional T cell signatures was only observed upon M-peptide stimulation. Moreover, signatures of natural RSV infections were identified from the data available in the public domain to investigate similarities between transcriptional signatures from PBMCs and upon peptide stimulation. This analysis also suggested activation of T cell response upon M-peptide stimulation. Hence, based on transcriptional response, markers were chosen to validate the role of M-peptide in activation of T cells. Indeed, CD4+CXCL9+ cells were identified upon M-peptide stimulation by flow cytometry. Future work using additional markers identified in this study could reveal additional unconventional T cells responding to RSV infections in infants. In conclusion, T cell responses to RSV in infants may not follow the canonical Th1/Th2 patterns of effector responses but include additional functions that may be unique to the neonatal period and correlate with clinical outcomes.

Abstract Background Postnatal development of early life microbiota influences immunity, metabolism, neurodevelopment, and infant health. Microbiome development occurs at multiple body sites, with distinct community compositions and functions. Associations between microbiota at multiple sites represent an unexplored influence on the infant microbiome. Here, we examined co-occurrence patterns of gut and respiratory microbiota in pre- and full-term infants over the first year of life, a period critical to neonatal development. Results Gut and respiratory microbiota collected as longitudinal rectal, throat, and nasal samples from 38 pre-term and 44 full-term infants were first clustered into community state types (CSTs) on the basis of their compositional profiles. Multiple methods were used to relate the occurrence of CSTs to temporal microbiota development and measures of infant maturity, including gestational age (GA) at birth, week of life (WOL), and post-menstrual age (PMA). Manifestation of CSTs followed one of three patterns with respect to infant maturity: (1) chronological, with CST occurrence frequency solely a function of post-natal age (WOL), (2) idiosyncratic to maturity at birth, with the interval of CST occurrence dependent on infant post-natal age but the frequency of occurrence dependent on GA at birth, and (3) convergent, in which CSTs appear first in infants of greater maturity at birth, with occurrence frequency in pre-terms converging after a post-natal interval proportional to pre-maturity. The composition of CSTs was highly dissimilar between different body sites, but the CST of any one body site was highly predictive of the CSTs at other body sites. There were significant associations between the abundance of individual taxa at each body site and the CSTs of the other body sites, which persisted after stringent control for the non-linear effects of infant maturity. Canonical correlations exist between the microbiota composition at each pair of body sites, with the strongest correlations between proximal locations. Conclusion These findings suggest that early microbiota is shaped by neonatal innate and adaptive developmental responses. Temporal progression of CST occurrence is influenced by infant maturity at birth and post-natal age. Significant associations of microbiota across body sites reveal distal connections and coordinated development of the infant microbial ecosystem.

HIV is capable of invading the brain soon after seroconversion. This ultimately can lead to deficits in multiple cognitive domains commonly referred to as HIV-associated neurocognitive disorders (HAND). Clinical diagnosis of such deficits requires detailed neuropsychological assessment but clinical signs may be difficult to detect during asymptomatic injury of the central nervous system (CNS). Therefore neuroimaging biomarkers are of particular interest in HAND. In this study, we constructed brain connectivity profiles of 40subjects (20 HIV positive subjects and 20 age-matched seronegative controls) using two different methods: a non-linear mutual connectivity analysis approach and a conventional method based on Pearson's correlation. These profiles were then summarized using graph-theoretic methods characterizing their topological network properties. Standard clinical and laboratory assessments were performed and a battery of neuropsychological (NP) tests was administered for all participating subjects. Based on NP testing, 14 of the seropositive subjects exhibited mild neurologic impairment. Subsequently, we analyzed associations between the network derived measures and neuropsychological assessment scores as well as common clinical laboratory plasma markers (CD4 cell count, HIV RNA) after adjusting for age and gender. Mutual connectivity analysis derived graph-theoretic measures, Modularity and Small Worldness, were significantly (p

Abstract Background Identification of factors that influence the neonatal gut microbiome is urgently needed to guide clinical practices that support growth of healthy preterm infants. Here, we examined the influence of nutrition and common practices on the gut microbiota and growth in a cohort of preterm infants. Results With weekly gut microbiota samples spanning postmenstrual age (PMA) 24 to 46 weeks, we developed two models to test associations between the microbiota, nutrition and growth: a categorical model with three successive microbiota phases (P1, P2, and P3) and a model with two periods (early and late PMA) defined by microbiota composition and PMA, respectively. The more significant associations with phase led us to use a phase-based framework for the majority of our analyses. Phase transitions were characterized by rapid shifts in the microbiota, with transition out of P1 occurring nearly simultaneously with the change from meconium to normal stool. The rate of phase progression was positively associated with gestational age at birth, and delayed transition to a P3 microbiota was associated with growth failure. We found distinct bacterial metabolic functions in P1–3 and significant associations between nutrition, microbiota phase, and infant growth. Conclusion The phase-dependent impact of nutrition on infant growth along with phase-specific metabolic functions suggests a pioneering potential for improving growth outcomes by tailoring nutrient intake to microbiota phase.

Abstract Background Normalization is an important data preparation step in gene expression analyses, designed to remove various systematic noise. Sample variance is greatly reduced after normalization, hence the power of subsequent statistical analyses is likely to increase. On the other hand, variance reduction is made possible by borrowing information across all genes, including differentially expressed genes (DEGs) and outliers, which will inevitably introduce some bias. This bias typically inflates type I error; and can reduce statistical power in certain situations. In this study we propose a new differential expression analysis pipeline, dubbed as super-delta, that consists of a multivariate extension of the global normalization and a modified t-test. A robust procedure is designed to minimize the bias introduced by DEGs in the normalization step. The modified t-test is derived based on asymptotic theory for hypothesis testing that suitably pairs with the proposed robust normalization. Results We first compared super-delta with four commonly used normalization methods: global, median-IQR, quantile, and cyclic loess normalization in simulation studies. Super-delta was shown to have better statistical power with tighter control of type I error rate than its competitors. In many cases, the performance of super-delta is close to that of an oracle test in which datasets without technical noise were used. We then applied all methods to a collection of gene expression datasets on breast cancer patients who received neoadjuvant chemotherapy. While there is a substantial overlap of the DEGs identified by all of them, super-delta were able to identify comparatively more DEGs than its competitors. Downstream gene set enrichment analysis confirmed that all these methods selected largely consistent pathways. Detailed investigations on the relatively small differences showed that pathways identified by super-delta have better connections to breast cancer than other methods. Conclusions As a new pipeline, super-delta provides new insights to the area of differential gene expression analysis. Solid theoretical foundation supports its asymptotic unbiasedness and technical noise-free properties. Implementation on real and simulated datasets demonstrates its decent performance compared with state-of-art procedures. It also has the potential of expansion to be incorporated with other data type and/or more general between-group comparison problems.

Fully convolutional networks (FCN) such as UNet and DeepLabv3+ are highly competitive when being applied in the detection of earthquake-damaged buildings in very high-resolution (VHR) remote sensing images. However, existing methods show some drawbacks, including incomplete extraction of different sizes of buildings and inaccurate boundary prediction. It is attributed to a deficiency in the global context-aware and inaccurate correlation mining in the spatial context as well as failure to consider the relative positional relationship between pixels and boundaries. Hence, a detection method for earthquake-damaged buildings based on the object contextual representations (OCR) and boundary enhanced loss (BE loss) was proposed. At first, the OCR module was separately embedded into high-level feature extractions of the two networks DeepLabv3+ and UNet in order to enhance the feature representation; in addition, a novel loss function, that is, BE loss, was designed according to the distance between the pixels and boundaries to force the networks to pay more attention to the learning of the boundary pixels. Finally, two improved networks (including OB-DeepLabv3+ and OB-UNet) were established according to the two strategies. To verify the performance of the proposed method, two benchmark datasets (including YSH and HTI) for detecting earthquake-damaged buildings were constructed according to the post-earthquake images in China and Haiti in 2010, respectively. The experimental results show that both the embedment of the OCR module and application of BE loss contribute to significantly increasing the detection accuracy of earthquake-damaged buildings and the two proposed networks are feasible and effective.

Background: Gene regulatory networks are complex dynamic systems and the reverse-engineering of such networks from high-dimensional time course transcriptomic data have attracted researchers from various fields. It is also interesting and important to study the behavior of the reconstructed networks on the basis of dynamic models and the biological mechanisms. We focus on the gene regulatory networks reconstructed using the ordinary differential equation (ODE) modelling approach and investigate the properties of these networks. Results: Controllability and stability analyses are conducted for the reconstructed gene response networks of 17 influenza infected subjects based on ODE models. Symptomatic subjects tend to have larger numbers of driver nodes, higher proportions of critical links and lower proportions of redundant links than asymptomatic subjects. We also show that the degree distribution, rather than the structure of networks, plays an important role in controlling the network in response to influenza infection. In addition, we find that the stability of high-dimensional networks is very sensitive to randomness in the reconstructed systems brought by errors in measurements and parameter estimation. Conclusions: The gene response networks of asymptomatic subjects are easier to be controlled than those of symptomatic subjects. This may indicate that the regulatory systems of asymptomatic subjects are easier to recover from disease stimulations, so these subjects are less likely to develop symptoms. Our results also suggest that stability constraint should be considered in the modelling of high-dimensional networks and the estimation of network parameters.

Quantitative measurement of localized longitudinal changes in brain abnormalities at an individual level may offer critical information for disease diagnosis and treatment. The voxel-wise permutation-based method SPREAD/iSPREAD, which combines resampling and spatial regression of neighboring voxels, provides an effective and robust method for detecting subject-specific longitudinal changes within the whole brain, especially for longitudinal studies with a limited number of scans. As an extension of SPREAD/iSPREAD, we present a general method that facilitates analysis of serial Diffusion Tensor Imaging (DTI) measurements (with more than two time points) for testing localized changes in longitudinal studies. Two types of voxel-level test statistics (model-free test statistics, which measure intra-subject variability across time, and test statistics based on general linear model that incorporate specific lesion evolution models) were estimated and tested against the null hypothesis among groups of DTI data across time. The implementation and utility of the proposed statistical method were demonstrated by both Monte Carlo simulations and applications on clinical DTI data from human brain in vivo. By a design of test statistics based on the disease progression model, it was possible to apportion the true significant voxels attributed to the disease progression and those caused by underlying anatomical differences that cannot be explained by the model, which led to improvement in false positive (FP) control in the results. Extension of the proposed method to include other diseases or drug effect models, as well as the feasibility of global statistics, was discussed. The proposed statistical method can be extended to a broad spectrum of longitudinal studies with carefully designed test statistics, which helps to detect localized changes at the individual level.

Medical researchers are increasingly interested in knowing how the complex community of micro-organisms living on human body impacts human health. Key to this is to understand how the microbes interact with each other. Time-course studies on human microbiome indicate that the composition of microbiome changes over short time periods, primarily as a consequence of synergistic and antagonistic interactions of the members of the microbiome with each other and with the environment. Knowledge of the abundance of bacteria-which are the predominant members of the human microbiome-in such time-course studies along with appropriate mathematical models will allow us to identify key dynamic interaction networks within the microbiome. However, the high-dimensional nature of these data poses significant challenges to the development of such mathematical models. We propose a high-dimensional linear State Space Model (SSM) with a new Expectation-Regularization-Maximization (ERM) algorithm to construct a dynamic Microbial Interaction Network (MIN). System noise and measurement noise can be separately specified through SSMs. In order to deal with the problem of high-dimensional parameter space in the SSMs, the proposed new ERM algorithm employs the idea of the adaptive LASSO-based variable selection method so that the sparsity property of MINs can be preserved. We performed simulation studies to evaluate the proposed ERM algorithm for variable selection. The proposed method is applied to identify the dynamic MIN from a time-course vaginal microbiome study of women. This method is amenable to future developments, which may include interactions between microbes and the environment.

A hallmark of the immune response to influenza is repeated encounters with proteins containing both genetically conserved and variable components. Therefore, the B and T cell repertoire is continually being remodeled, with competition between memory and naïve lymphocytes. Our previous work using a mouse model of secondary heterosubtypic influenza infection has shown that this competition results in a focusing of CD4 T cell response specificity towards internal virion proteins with a selective decrease in CD4 T cell reactivity to the novel HA epitopes. Strikingly, this shift in CD4 T cell specificity was associated with a diminished anti-HA antibody response. Here, we sought to determine whether the loss in HA-specific reactivity that occurs as a consequence of immunological memory could be reversed by selectively priming HA-specific CD4 T cells prior to secondary infection. Using a peptide-based priming strategy, we found that selective expansion of the anti-HA CD4 T cell memory repertoire enhanced HA-specific antibody production upon heterosubtypic infection. These results suggest that the potentially deleterious consequences of repeated exposure to conserved influenza internal virion proteins could be reversed by vaccination strategies that selectively arm the HA-specific CD4 T cell compartment. This could be a potentially useful pre-pandemic vaccination strategy to promote accelerated neutralizing antibody production on challenge with a pandemic influenza strain that contains few conserved HA epitopes.

A novel adaptive morphological attribute profile under object boundary constraint (AMAP–OBC) method is proposed in this study for automatic building extraction from high-resolution remote sensing (HRRS) images. By investigating the associated attributes in morphological attribute profiles (MAPs), the proposed method establishes corresponding relationships between AMAP–OBC and building characteristics in HRRS images. In the preprocessing step, the candidate object set is extracted by a group of rules for screening of non-building objects. Second, based on the proposed adaptive scale parameter extraction and object boundary constraint strategies, AMAP–OBC is conducted to obtain the initial building set. Finally, a further identification strategy with adaptive threshold combination is proposed to obtain the final building extraction results. Through experiments of multiple groups of HRRS images from different sensors, the proposed method shows outstanding performance in terms of automatic building extraction from diverse geographic objects in urban scenes.

Current approaches to study transcriptional profiles post influenza infection typically rely on tissue sampling from one or two sites at a few time points, such as spleen and lung in murine models. In this study, we infected female C57/BL6 mice intranasally with mouse-adapted H3N2/Hong Kong/X31 avian influenza A virus, and then analyzed the gene expression profiles in four different compartments (blood, lung, mediastinal lymph nodes, and spleen) over 11 consecutive days post infection. These data were analyzed by an advanced statistical procedure based on ordinary differential equation (ODE) modeling. Vastly different lists of significant genes were identified by the same statistical procedure in each compartment. Only 11 of them are significant in all four compartments. We classified significant genes in each compartment into co-expressed modules based on temporal expression patterns. We then performed functional enrichment analysis on these co-expression modules and identified significant pathway and functional motifs. Finally, we used an ODE based model to reconstruct gene regulatory network (GRN) for each compartment and studied their network properties.