Molecular mechanism of saturated aldehyde oxidation: A DFT insight into volatiles forming from decanal thermal oxidation.

Decanal is one of the main products of lipid oxidation. It has been shown that decanal can oxidize to form volatiles with shorter carbon chains during heating, but the mechanism is still unclear. In this study, volatile compounds formed in the decanal thermal oxidation were verified using thermal-desorption cryo-trapping combined with GC-MS. A total of 32 volatile compounds were identified. The oxidation mechanism of decanal was studied by applying density functional theory. Results revealed that the carbonyl carbon atom was the thermal oxidation site of decanal and two pathways of peroxide oxidation were determined: the ortho­carbon and the meta­carbon oxidation. The ortho­carbon oxidation pathway is superior to the occurrence of the meta­carbon oxidation pathway. The oxidative mechanism of decanal was finally summarized as the peroxide oxidation based on radical attack on the carbonyl carbon, which would provide a theoretical basis for exploring the oxidation mechanism of other saturated aldehydes.

Mechanism of thermal oxidation into volatile compounds from (E)-4-decenal: A density functional theory study.

Unsaturated aliphatic aldehyde oxidation plays a significant role in the deep oxidation of fatty acids to produce volatile chemicals. Exposing the oxidation process of unsaturated aliphatic aldehydes is crucial to completely comprehend how food flavor forms. In this study, thermal desorption cryo-trapping in conjunction with gas chromatography-mass spectrometry was used to examine the volatile profile of (E)-4-decenal during heating, and 32 volatile compounds in all were detected and identified. Meanwhile, density functional theory (DFT) calculations were used, and 43 reactions were obtained in the 24 pathways, which were summarized into the peroxide reaction mechanism (ROOH), the peroxyl radical reaction mechanism (ROO·) and the alkoxy radical reaction mechanism (RO·). Moreover, the priority of these three oxidative mechanisms was the RO· mechanism > ROOH mechanism > ROO· mechanism. Furthermore, the DFT results and experimental results agreed well, and the oxidative mechanism of (E)-4-decenal was finally illuminated.

Density functional theory studies on the oleic acid thermal oxidation into volatile compounds.

Oleic acid oxidation is one of the main sources of food flavor compounds. Volatile profiling was investigated using thermal desorption cryo-trapping combined with gas chromatography-mass spectrometry to analyze the volatile composition of oleic acid oxidation. A total of 43 volatile compounds, including aldehydes (11), ketones (2), alcohols (5), furans (2), acids (8), ester (12) and alkane (3) were identified from oleic acid during heating. Then, density functional theory (DFT) was applied to analyze the oxidative mechanism of oleic acid during heating. A total of 30 reactions were obtained and grouped into the peroxide (ROOH), alkoxy radical (RO•), and peroxide radical (ROO•) pathways. The structures of intermediates, transition states (TS), and products in each reaction were also determined. Results show that the branch chemical reactions were the key reactions in different reaction pathway. Moreover, the reaction priority of the thermal oxidation reaction of oleic acid was the peroxide radical mechanism > the peroxide mechanism > the alkoxy radical mechanism.

Deep exploration of lipid oxidation into flavor compounds: A density functional theory study on (E)-2-decenal thermal oxidative reaction.

Unsaturated fatty aldehydes are the main products of fatty acid oxidation, and could be further oxidized to form volatile compounds with shorter carbon chains. Therefore, studying the oxidation of unsaturated fatty aldehydes is an important way to reveal the mechanism of food flavor formation during heating. In this study, volatile profiling of (E)-2-decenal during heating was firstly investigated by using thermal-desorption cryo-trapping combined with gas chromatography-mass spectrometry (GC-MS). A total of 38 volatile compounds were detected. Then, twenty-one reactions in the heating process of (E)-2-decenal were obtained by using density functional theory (DFT) calculations, and grouped into three oxidation pathways, namely, peroxide pathway, peroxyl radical pathway and alkoxy radical pathway. Meanwhile, the priority of these three pathways was the alkoxy radical reaction pathway > peroxide pathway > peroxyl radical reaction pathway. Moreover, the calculated results agreed well with the experimental results.

Supramolecular assemblies with spatio-temporal sequential drug delivery capability treat spinal cord injury via neuroprotection and immunoregulation.

Spinal cord injury (SCI) can result in irreversible motor and sensory deficits. However, up to data, clinical first-line drugs have ambiguous benefits and debilitating side effects, mainly due to the insufficient accumulation, poor physiological barrier penetration, and lack of spatio-temporal controlled release at lesion tissue. Herein, we proposed a supramolecular assemblies composed of hyperbranched polymer-formed core/shell structure through host-guest interactions. Such HPAA-BM@CD-HPG-C assemblies co-loaded with p38 inhibitor (SB203580) and insulin-like growth factor 1(IGF-1) are able to achieve time- and space-programmed sequential delivery benefiting from their cascaded responsiveness. The core-shell disassembly of HPAA-BM@CD-HPG-C occurs in acidic micro-environment around lesion, achieving preferentially the burst release of IGF-1 to protect survival neurons. Subsequently, the HPAA-BM cores containing SB203580 are endocytosed by the recruited macrophages and degraded by intracellular GSH, accelerating the release of SB203580 to promote the conversion from M1 to M2 macrophage. Hence, the successive synergy of neuroprotection and immunoregulation effects contribute to subsequent nerve repair and locomotor recovery as demonstrated in vitro and in vivo studies. Thus, our fabrication provides a strategy that multiple drugs co-delivery in a spatio-temporal selective manner adapting to the disease progression through self-cascaded disintegration, are expected to realize multidimensional precise treatment of SCI.

The diagnostic value of CT-guided percutaneous puncture biopsy of pulmonary ground-glass nodules: a meta-analysis.

BACKGROUND: More and more pulmonary ground-glass nodules (GGNs) are screened with the extensive usage of low-dose computed tomography (CT). The need of CT-guided percutaneous puncture biopsy of GGN remains controversial. PURPOSE: To explore the diagnostic accuracy of CT-guided percutaneous puncture biopsy of GGNs. MATERIAL AND METHODS: We searched PubMed, EMBASE, the Cochrane Library, and CNKI. Included studies reported the puncture biopsy results of pulmonary GGNs, including the number of true positive (TP), false positive (FP), true negative (TN), and false negative (FN) cases. After evaluating the studies, statistical analysis, and quality assessment, the pooled diagnostic sensitivity (SEN), specificity (SPE), and diagnostic odds ratio (DOR) were calculated. The summary receiver operating characteristic (SROC) curve was constructed and the area under the curve (AUC) was calculated. Subgroup analysis was performed according to whether spiral CT or fluoroscopy-guided CT was used in the study. RESULTS: This meta-analysis included 14 studies with a total of 759 patients (702 samples). The pooled SEN, SPE, and DOR of CT-guided puncture biopsy of pulmonary GGNs were 0.91 (95% confidence interval [CI] = 0.89-0.94), 0.99 (95% CI = 0.95-1.00), and 138.72 (95% CI = 57.98-331.89), respectively. The AUC was 0.97. CONCLUSION: Our results indicated that CT-guided puncture biopsy of GGNs has high SEN, SPE, and DOR, which proved that CT-guided puncture biopsy was a good way to determine the pathological nature of GGN.

Development and Validation of a CT-Based Radiomics Nomogram in Patients With Anterior Mediastinal Mass: Individualized Options for Preoperative Patients.

Background: To improve the preoperative diagnostic accuracy and reduce the non-therapeutic thymectomy rate, we established a comprehensive predictive nomogram based on radiomics data and computed tomography (CT) features and further explored its potential use in clinical decision-making for anterior mediastinal masses (AMMs). Methods: A total of 280 patients, including 280 with unenhanced CT (UECT) and 241 with contrast-enhanced CT (CECT) scans, all of whom had undergone thymectomy for AMM with confirmed histopathology, were enrolled in this study. A total of 1,288 radiomics features were extracted from each labeled mass. The least absolute shrinkage and selection operator model was used to select the optimal radiomics features in the training set to construct the radscore. Multivariate logistic regression analysis was conducted to establish a combined clinical radiographic radscore model, and an individualized prediction nomogram was developed. Results: In the UECT dataset, radscore and the UECT ratio were selected for the nomogram. The combined model achieved higher accuracy (AUC: 0.870) than the clinical model (AUC: 0.752) for the prediction of therapeutic thymectomy probability. In the CECT dataset, the clinical and combined models achieved higher accuracy (AUC: 0.851 and 0.836, respectively) than the radscore model (AUC: 0.618) for the prediction of therapeutic thymectomy probability. Conclusions: In patients who underwent UECT only, a nomogram integrating the radscore and the UECT ratio achieved good accuracy in predicting therapeutic thymectomy in AMMs. However, the use of radiomics in patients with CECT scans did not improve prediction performance; therefore, a clinical model is recommended.

A nomogram predicting the severity of COVID-19 based on initial clinical and radiologic characteristics.

Aim: This study aimed to build an easy-to-use nomogram to predict the severity of COVID-19. Patients & methods: From December 2019 to January 2020, patients confirmed with COVID-19 in our hospital were enrolled. The initial clinical and radiological characteristics were extracted. Univariate and multivariate logistic regression were used to identify variables for the nomogram. Results: In total, 104 patients were included. Based on statistical analysis, age, levels of neutrophil count, creatinine, procalcitonin and numbers of involved lung segments were identified for nomogram. The area under the curve was 0.939 (95% CI: 0.893-0.984). The calibration curve showed good agreement between prediction of nomogram and observation in the primary cohort. Conclusion: An easy-to-use nomogram with great discrimination was built to predict the severity of COVID-19.

Comprehensive metabolite analysis of wheat dough in a continuous heating process.

Comprehensive metabolite analysis was carried out by gas chromatography combined with mass spectrometry to investigate the time-dependent metabolic changes during wheat dough heating. Thirty-five volatile metabolites comprising alcohols, ketones, aldehydes, aromatic compounds, furans, acids and esters were identified. Sixty-four non-volatile metabolites, which covered a broad spectrum of polar and non-polar constituents, were also identified and quantified. Results showed that the content of most volatile metabolites increased during heating. Meanwhile, the levels of non-volatile polar metabolites, such as sugar and amino acid, increased and the levels of non-volatile non-polar metabolites decreased or remained constant, including fatty acid methyl ester and free fatty acids. PCA results demonstrated that metabolic changes could be reflected by time-dependent shifts in the PCA loading scores during heating. Analysis of the loadings further showed that most volatile metabolites and non-volatile polar metabolites were the major contributors of the heating time-driven changes during heating. Furthermore, lipid oxidation mainly occurred in the residues of oleic acid and linoleic acid of triglycerides.

Development and Validation of a Concise Prediction Scoring System for Asian Lung Cancer Patients with EGFR Mutation Before Treatment.

Purpose We aimed to determine the epidermal growth factor receptor (EGFR) genetic profile of lung cancer in Asians, and develop and validate a non-invasive prediction scoring system for EGFR mutation before treatment. Methods This was a single-center retrospective cohort study using data of patients with lung cancer who underwent EGFR detection (n = 1450) from December 2014 to October 2020. Independent predictors were filtered using univariate and multivariate logistic regression analyses. According to the weight of each factor, a prediction scoring system for EGFR mutation was constructed. The model was internally validated using bootstrapping techniques and temporally validated using prospectively collected data (n = 210) between November 2020 and June 2021.Results In 1450 patients with lung cancer, 723 single mutations and 51 compound mutations were observed in EGFR. Thirty-nine cases had two or more synchronous gene mutations. We developed a scoring system according to the independent clinical predictors and stratified patients into risk groups according to their scores: low-risk (score <4), moderate-risk (score 4-8), and high-risk (score >8) groups. The C-statistics of the scoring system model was 0.754 (95% CI 0.729-0.778). The factors in the validation group were introduced into the prediction model to test the predictive power of the model. The results showed that the C-statistics was 0.710 (95% CI 0.638-0.782). The Hosmer-Lemeshow goodness-of-fit showed that χ2 = 6.733, P = 0.566. Conclusions The scoring system constructed in our study may be a non-invasive tool to initially predict the EGFR mutation status for those who are not available for gene detection in clinical practice.

Comparison of CT-Guided Core Needle Biopsy in Pulmonary Ground-Glass and Solid Nodules Based on Propensity Score Matching Analysis.

Purpose: To compare the diagnostic accuracy and safety of computed tomography (CT)-guided core needle biopsy (CNB) between pulmonary ground-glass and solid nodules using propensity score matching (PSM) method and determine the relevant risk factors. Methods: This was a single-center retrospective cohort study using data from 665 patients who underwent CT-guided CNB of pulmonary nodules in our hospital between May 2019 and May 2021, including 39 ground-glass nodules (GGNs) and 626 solid nodules. We used a 1:4 PSM analysis to compared the diagnostic yields and complications rates of CT-guided CNB between 2 groups. Results: After PSM, 170 cases involved in the comparison (34 GGNs vs 136 solid nodules) were randomly matched (1:4) by patient demographics, clinical history, lesion characteristics, and procedure-related factors. There was no statistically significant difference in the diagnostic yields and complications rates between 2 groups. Significant pneumothorax incidence increase was noted at small lesion size, deep lesion location, and traversing interlobar fissure (P < .05). Post-biopsy hemorrhage was a protective factor for pneumothorax (P < .05). The size/proportion of consolidation of GGN did not influence the diagnostic accuracy and complication incidence (P > .05). Conclusions: The accuracy and safety of CT-guided CNB were comparable for ground-glass and solid nodules and the size/proportion of consolidation of GGN may be not a relevant risk factor. The biopsy should avoid traversing interlobar fissure as far as possible. Smaller lesion size and deeper lesion location may lead to higher pneumothorax rate and post-biopsy hemorrhage may be a protective factor for pneumothorax.

A Predictive Model to Differentiate Between Second Primary Lung Cancers and Pulmonary Metastasis.

RATIONALE AND OBJECTIVES: To develop and validate a nomogram for differentiating second primary lung cancers (SPLCs) from pulmonary metastases (PMs). MATERIALS AND METHODS: A total of 261 lesions from 253 eligible patients were included in this study. Among them, 195 lesions (87 SPLCs and 108 PMs) were used in the training cohort to establish the diagnostic model. Twenty-one clinical or imaging features were used to derive the model. Sixty-six lesions (32 SPLCs and 34 PMs) were included in the validation set. RESULTS: After analysis, age, lesion distribution, type of lesion, air bronchogram, contour, spiculation, and vessel convergence sign were considered to be significant variables for distinguishing SPLCs from PMs. Subsequently, these variables were selected to establish a nomogram. The model showed good distinction in the training set (area under the curve = 0.97) and the validation set (area under the curve = 0.92). CONCLUSION: This study found that the nomogram calculated from clinical and radiological characteristics could accurately classify SPLCs and PMs.

Radiomics Study for Discriminating Second Primary Lung Cancers From Pulmonary Metastases in Pulmonary Solid Lesions.

BACKGROUND: The objective of this study was to assess the value of quantitative radiomics features in discriminating second primary lung cancers (SPLCs) from pulmonary metastases (PMs). METHODS: This retrospective study enrolled 252 malignant pulmonary nodules with histopathologically confirmed SPLCs or PMs and randomly assigned them to a training or validation cohort. Clinical data were collected from the electronic medical records system. The imaging and radiomics features of each nodule were extracted from CT images. RESULTS: A rad-score was generated from the training cohort using the least absolute shrinkage and selection operator regression. A clinical and radiographic model was constructed using the clinical and imaging features selected by univariate and multivariate regression. A nomogram composed of clinical-radiographic factors and a rad-score were developed to validate the discriminative ability. The rad-scores differed significantly between the SPLC and PM groups. Sixteen radiomics features and four clinical-radiographic features were selected to build the final model to differentiate between SPLCs and PMs. The comprehensive clinical radiographic-radiomics model demonstrated good discriminative capacity with an area under the curve of the receiver operating characteristic curve of 0.9421 and 0.9041 in the respective training and validation cohorts. The decision curve analysis demonstrated that the comprehensive model showed a higher clinical value than the model without the rad-score. CONCLUSION: The proposed model based on clinical data, imaging features, and radiomics features could accurately discriminate SPLCs from PMs. The model thus has the potential to support clinicians in improving decision-making in a noninvasive manner.

Clinical, radiological, and laboratory characteristics and risk factors for severity and mortality of 289 hospitalized COVID-19 patients.

BACKGROUND: The coronavirus disease 2019 (COVID-19) has become a global pandemic, with 10%-20% of severe cases and over 508 000 deaths worldwide. OBJECTIVE: This study aims to address the risk factors associated with the severity of COVID-19 patients and the mortality of severe patients. METHODS: 289 hospitalized laboratory-confirmed COVID-19 patients were included in this study. Electronic medical records, including patient demographics, clinical manifestation, comorbidities, laboratory tests results, and radiological materials, were collected and analyzed. According to the severity and outcomes of the patients, they were divided into three groups: nonsurvived (n = 49), survived severe (n = 78), and nonsevere (n = 162) groups. Clinical, laboratory, and radiological data were compared among these groups. Principal component analysis (PCA) was applied to reduce the dimensionality and visualize the patients on a low-dimensional space. Correlations between clinical, radiological, and laboratory parameters were investigated. Univariate and multivariate logistic regression methods were used to determine the risk factors associated with mortality in severe patients. Longitudinal changes of laboratory findings of survived severe cases and nonsurvived cases during hospital stay were also collected. RESULTS: Of the 289 patients, the median age was 57 years (range, 22-88) and 155 (53.4%) patients were male. As of the final follow-up date of this study, 240 (83.0%) patients were discharged from the hospital and 49 (17.0%) patients died. Elder age, underlying comorbidities, and increased laboratory variables, such as leukocyte count, neutrophil count, neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), procalcitonin (PCT), D-dimer, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and blood urea nitrogen (BUN) on admission, were found in survived severe cases compared to nonsevere cases. According to the multivariate logistic regression analysis, elder age, a higher number of affected lobes, elevated CRP levels on admission, increased prevalence of chest tightness/dyspnea, and smoking history were independent risk factors for death of severe patients. A trajectory in PCA was observed from "nonsevere" toward "nonsurvived" via "severe and survived" patients. Strong correlations between the age of patients, the affected lobe numbers, and laboratory variables were identified. Dynamic changes of laboratory findings of survived severe cases and nonsurvived cases during hospital stay showed that continuing increase of leukocytes and neutrophil count, sustained lymphopenia and eosinopenia, progressing decrease in platelet count, as well as high levels of NLR, CRP, PCT, AST, BUN, and serum creatinine were associated with in-hospital death. CONCLUSIONS: Survived severe and nonsurvived COVID-19 patients had distinct clinical and laboratory characteristics, which were separated by principle component analysis. Elder age, increased number of affected lobes, higher levels of serum CRP, chest tightness/dyspnea, and smoking history were risk factors for mortality of severe COVID-19 patients. Longitudinal changes of laboratory findings may be helpful in predicting disease progression and clinical outcome of severe patients.

Metabolite analysis of wheat dough fermentation incorporated with buckwheat.

Dough fermentation represents an important developmental stage in the manufacturing process. In this study, volatile and nonvolatile metabolite analysis were carried out to investigate time-dependent metabolic changes in the course of wheat dough fermentation incorporated with buckwheat based on gas chromatography-mass spectrometry (GC/MS). A total of 70 nonvolatile metabolites were identified, covering a broad spectrum of polar (e.g., amino acids, sugars, sugar alcohols, and acids) and nonpolar (e.g., fatty acid methyl esters, free fatty acids, and sterols) low molecular weight dough constituents. Meanwhile, sixty-four volatile metabolites comprising aldehydes, ketones, alcohols, organic acids, aromatic compounds, and furans were identified using solid-phase micro-extraction combined with GC-MS. Some differences may exist in the volatile composition between fermented and unfermented dough. Statistical assessment of the nonvolatile data via principal component analysis demonstrated that the metabolic changes during the mixed dough fermentation are reflected by time-dependent shifts of polar nonvolatile metabolites. And some potential nutritional markers, such as amino acids and sugars, could be developed to optimize and control the industrial dough fermentation incorporated with buckwheat.

Risk Factor Analysis and Nomogram Construction for Non-Survivors among Critical Patients with COVID-19.

The outbreak of the coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, occurred in China in December 2019. This disease has caused more than 70,000 deaths worldwide. We intend to analyze the risk factors of death and establish a prognosis nomogram for critical patients with COVID-19. We analyzed the clinical data of COVID-19 patients in Zhongnan Hospital of Wuhan University who were in the critical state before March 20, 2020. Data were collected on admission and compared between survivors and non-survivors and analyzed by univariable and multivariable logistic regression analyses. Finally, 104 patients were included, 50 of whom died. Age (odds ratio, OR 5.73 [95% confidence interval, CI, 1.14-28.81]), chest tightness (OR 5.50 [95% CI, 1.02-9.64]), AST (OR 6.57 [95% CI, 1.33-32.48]), and blood urea nitrogen (5.59 [95% CI, 1.05-29.74]) at admission were considered predictors of the risk of death in critical patients and were selected to construct the nomogram. Subsequently, we established a nomogram model and validated it. The sensitivity and specificity of the nomogram were 96.0% and 74.1%, respectively. The area under the curve was 0.893 (95% CI, 0.807-0.980).

The effect of strontium modified rough titanium surface on biologic response of MC3T3-E1 cells.

It has been shown that strontium (Sr) promotes bone formation, reduces bone resorption. In the study, magnetron sputtering method was used for preparing Sr covered on sandblasted and acid-etched (SLA) titanium surface. Surface morphology was examined by scanning electron microscopy (SEM), chemical composition of surface was investigated by X-ray energy dispersive spectrometry (EDS). MC3T3-E1cells were cultured on Sr-SLA and SLA disks. Cell morphology was studied through confocal laser scanning microscope and SEM. Cell proliferation were measured by MTT assay. Degrees of mineralization and alkaline phosphatase (ALP) activity were calculated and compared. EDS and SEM results indicated that Sr ions were successfully loaded in Sr-SLA surface, whereas, Sr-SLA and SLA surfaces demonstrated similar typical isotropic irregular indentations. The MC3T3-E1 cells developed on Sr-SLA surface showed improved morphology, better proliferation as well as greater differentiation.These findings suggest that the modification with Sr incorporated in moderately rough surface has a favorable biocompatibility.

Contribution of host nucleoporin 62 in HIV-1 integrase chromatin association and viral DNA integration.

HIV-1 integration is promoted by viral integrase (IN) and its cellular cofactors. The lens epithelium-derived growth factor (LEDGF/p75), an IN interacting cellular cofactor, has been shown to play an important role in HIV-1 chromatin targeting and integration. However, whether other cellular cofactors are also involved in viral replication steps is still elusive. Here, we show that nucleoporin 62 (Nup62) is a chromatin-bound protein and can specifically interact with HIV-1 IN in both soluble nuclear extract and chromatin-bound fractions. The knockdown of Nup62 by shRNA reduced the association of IN with host chromatin and significantly impaired viral integration and replication in HIV-1-susceptible cells. Furthermore, the expression of the IN-binding region of Nup62 in CD4(+) T cells significantly inhibited HIV-1 infection. Taken together, these results indicate that the cellular Nup62 is specifically recruited by HIV-1 IN and contribute to an efficient viral DNA integration.

Host protein Ku70 binds and protects HIV-1 integrase from proteasomal degradation and is required for HIV replication.

HIV-1 integrase (IN) is a key viral enzymatic protein acting in several viral replication steps, including integration. IN has been shown to be an unstable protein degraded by the N-end rule pathway through the host ubiquitin-proteasome machinery. However, it is still not fully understood how this viral protein is protected from the host ubiquitin-proteasome system within cells during HIV replication. In the present study, we provide evidence that the host protein Ku70 interacts with HIV-1 IN and protects it from the Lys(48)-linked polyubiquitination proteasomal pathway. Moreover, Ku70 is able to down-regulate the overall protein polyubiquitination level within the host cells and to specifically deubiquitinate IN through their interaction. Mutagenic studies revealed that the C terminus of IN (residues 230-288) is required for IN binding to the N-terminal part of Ku70 (Ku70(1-430)), and their interaction is independent of Ku70/80 heterodimerization. Finally, knockdown of Ku70 expression in both virus-producing and target CD4(+) T cells significantly disrupted HIV-1 replication and rendered two-long terminal repeat circles and integration undetectable, indicating that Ku70 is required for both the early and the late stages of the HIV-1 life cycle. Interestingly, Ku70 was incorporated into the progeny virus in an IN-dependent way. We proposed that Ku70 may interact with IN during viral assembly and accompany HIV-1 IN upon entry into the new target cells, acting to 1) protect IN from the host defense system and 2) assist IN integration activity. Overall, this report provides another example of how HIV-1 hijacks host cellular machinery to protect the virus itself and to facilitate its replication.