Durability Analysis of Cold Spray Repairs: Phase I-Effect of Surface Grit Blasting.

This paper presents the results of an extensive investigation into the durability of cold spray repairs to corrosion damage in AA7075-T7351 aluminium alloy specimens where, prior to powder deposition, the surface preparation involved grit blasting. In this context, it is shown that the growth of small naturally occurring cracks in cold spray repairs to simulated corrosion damage can be accurately computed using the Hartman-Schijve crack growth equation in a fashion that is consistent with the requirements delineated in USAF Structures Bulletin EZ-SB-19-01, MIL-STD-1530D, and the US Joint Services Structural Guidelines JSSG2006. The relatively large variation in the da/dN versus ΔK curves associated with low values of da/dN highlights the fact that, before any durability assessment of a cold spray repair to an operational airframe is attempted, it is first necessary to perform a sufficient number of tests so that the worst-case small crack growth curve needed to perform the mandated airworthiness certification analysis can be determined.

Characterization of the Benefits and Risks of Therapeutic Anticoagulation in Patients Admitted with Severe COVID-19 (CRITAC).

Background: Severe COVID-19 is associated with increased rates of thrombotic complications. Recent provincial recommendations in British Columbia have suggested providing thromboprophylaxis with therapeutic anticoagulation for hospital inpatients with severe COVID-19 who do not have a high risk of bleeding. Objectives: To characterize the rates of major bleeding, thrombotic events, complications from COVID-19, and adverse effects among patients with severe COVID-19 treated with therapeutic anticoagulation. Methods: This retrospective chart review involved patients with laboratory-confirmed COVID-19 who were admitted to 3 sites within a local health authority between April 1 and December 31, 2021, and received therapeutic anticoagulation for thromboprophylaxis. Results: After screening of 1036 patients, 72 patients were included in the study. The mean age of participants was 54 years, 63% (n = 45) were male, and 92% (n = 66) were receiving supplemental oxygen by nasal prongs on admission. The primary outcome, major bleeding, was experienced by 1 patient (1%). Increasing oxygen requirements resulting in progression to high-flow nasal cannula occurred in 11 patients (15%), and 5 patients (7%) required admission to the intensive care unit. One patient (1%) experienced a thrombotic event, and 1 patient (1%) had a minor bleed. The mean duration of hospitalization was 10 (standard deviation 10.8) days. One death occurred during the study period, and no cases of heparin-induced thrombocytopenia were observed. Conclusions: In this study of hospital inpatients with severe COVID-19 who were deemed to be at low risk of bleeding and who received therapeutic anticoagulation, there were low rates of both major bleeding and thrombotic events.

Contexte: Les cas graves de COVID-19 sont associés à des taux accrus de complications thrombotiques. De récentes recommandations provinciales en Colombie-Britannique proposent de fournir une thromboprophylaxie avec anticoagulation thérapeutique aux patients hospitalisés atteints d'une forme grave de COVID-19 qui ne présentent pas un risque élevé de saignement. Objectifs: Caractériser les taux d'hémorragies majeures, d'événements thrombotiques, de complications découlant de la COVID-19 et d'effets indésirables chez les patients atteints de COVID-19 sévère traités par anticoagulation thérapeutique. Méthodes: Cette revue rétrospective des dossiers portait sur des patients atteints de COVID-19 confirmée en laboratoire qui ont été admis dans 3 sites au sein d'une autorité sanitaire locale entre le 1er avril et le 31 décembre 2021 et qui ont reçu une anticoagulation thérapeutique pour la thromboprophylaxie. Résultats: Après la présélection de 1036 patients, 72 ont été inclus dans l'étude. L'âge moyen des participants était de 54 ans, 63 % (n = 45) étaient des hommes et 92 % (n = 66) recevaient un supplément d'oxygène par sonde nasale à l'admission. Le critère de jugement principal, une hémorragie majeure, a été observé chez 1 patient (1 %). Une augmentation des besoins en oxygène entraînant une progression vers une canule nasale à haut débit s'est produite chez 11 patients (15 %) et 5 patients (7 %) ont dû être admis à l'unité de soins intensifs. Un patient (1 %) a présenté un événement thrombotique et 1 patient (1 %) a eu un saignement mineur. La durée moyenne d'hospitalisation était de 10 jours (écart type 10,8). Un décès est survenu au cours de la période d'étude et aucun cas de thrombocytopénie induite par l'héparine n'a été observé. Conclusions: Dans cette étude portant sur des patients hospitalisés atteints d'une forme grave de COVID-19, considérés comme présentant un faible risque de saignement et ayant reçu une anticoagulation thérapeutique, les taux d'hémorragies majeures et d'événements thrombotiques étaient faibles.

Synthesis of 2-benzyl benzoxazoles and benzothiazoles via elemental sulfur promoted cyclization of styrenes with 2-nitrophenols and N,N-dialkyl-3-nitroanilines.

Herein we report a method for affording 2-benzyl benzoxazoles from substituted styrenes and 2-nitrophenols. The success of this method relies on the use of simple reagents, namely elemental sulfur and DABCO. A combination of identical reagents was utilized for the annulation of styrenes with N,N-dialkyl-3-nitroanilines to afford 2-benzyl benzothiazoles. Overall, benzoxazoles and benzothiazoles bearing useful functionalities such as halogens, amines, and heterocyclic groups were isolated in moderate to good yields. Our methods are a rare example of divergent transformations of substituted nitroarenes towards 2-benzyl benzoxazoles and benzothiazoles.

Utilizing multimodal approach to identify candidate pathways and biomarkers and predicting frailty syndrome in individuals from UK Biobank.

Frailty, a prevalent clinical syndrome in aging adults, is characterized by poor health outcomes, represented via a standardized frailty-phenotype (FP), and Frailty Index (FI). While the relevance of the syndrome is gaining awareness, much remains unclear about its underlying biology. Further elucidation of the genetic determinants and possible underlying mechanisms may help improve patients' outcomes allowing healthy aging.Genotype, clinical and demographic data of subjects (aged 60-73 years) from UK Biobank were utilized. FP was defined on Fried's criteria. FI was calculated using electronic-health-records. Genome-wide-association-studies (GWAS) were conducted and polygenic-risk-scores (PRS) were calculated for both FP and FI. Functional analysis provided interpretations of underlying biology. Finally, machine-learning (ML) models were trained using clinical, demographic and PRS towards identifying frail from non-frail individuals.Thirty-one loci were significantly associated with FI accounting for 12% heritability. Seventeen of those were known associations for body-mass-index, coronary diseases, cholesterol-levels, and longevity, while the rest were novel. Significant genes CDKN2B and APOE, previously implicated in aging, were reported to be enriched in lipoprotein-particle-remodeling. Linkage-disequilibrium-regression identified specific regulation in limbic-system, associated with long-term memory and cognitive-function. XGboost was established as the best performing ML model with area-under-curve as 85%, sensitivity and specificity as 0.75 and 0.8, respectively.This study provides novel insights into increased vulnerability and risk stratification of frailty syndrome via a multi-modal approach. The findings suggest frailty as a highly polygenic-trait, enriched in cholesterol-remodeling and metabolism and to be genetically associated with cognitive abilities. ML models utilizing FP and FI + PRS were established that identified frailty-syndrome patients with high accuracy.

Mobilization of Heavy Metals in a Saline Confined Aquifer as a Consequence of Rainwater Injection: A Case Study in Southern Vietnam.

An artificial recharge test was performed in Ho Chi Minh City, Vietnam to see the geochemical response of a saline coastal plain aquifer to the injected rainwater. The results show that the rainwater injection can cause mobilization of heavy metals due to pyrite oxidation and this phenomenon can persist even after the full recovery of the injected water. In this study, a 30-m-deep well was installed in a confined aquifer. Pyrite framboids were observed in the sediment samples collected during the well drilling. A total of 400 L rainwater was injected into the well for 70 min. After waiting 63 h, the well was extracted at a pump speed of 2.7 L/min and the chemistry of the pumped groundwater was monitored for 10 h. The groundwater showed geochemical features close to rainwater at the early stage of pumping and gradually changed to those of the background waters, especially, in electrical conductivity and Cl- concentration, as the pumping proceeded. However, the groundwater pumped in the later stage showed much increased concentrations in SO42-, total iron (FeT), AsT, Ni, Mn and Zn relative to the calculated mixing concentrations due to pyrite oxidation even though NO3-, the pyrite oxidant, already had disappeared. It was revealed from the geochemical modeling that the persistent pyrite oxidation was the result of the reaction with ferrihydrite, which precipitated in pores of the sediment by the injection of aerated water. We believe our study is a good example showing the importance of careful design of the artificial recharge systems to avoid or minimize the geochemical disturbance of aquifer.

Deep Learning Empowers Endoscopic Detection and Polyps Classification: A Multiple-Hospital Study.

The present study aimed to develop an AI-based system for the detection and classification of polyps using colonoscopy images. A total of about 256,220 colonoscopy images from 5000 colorectal cancer patients were collected and processed. We used the CNN model for polyp detection and the EfficientNet-b0 model for polyp classification. Data were partitioned into training, validation and testing sets, with a 70%, 15% and 15% ratio, respectively. After the model was trained/validated/tested, to evaluate its performance rigorously, we conducted a further external validation using both prospective (n = 150) and retrospective (n = 385) approaches for data collection from 3 hospitals. The deep learning model performance with the testing set reached a state-of-the-art sensitivity and specificity of 0.9709 (95% CI: 0.9646-0.9757) and 0.9701 (95% CI: 0.9663-0.9749), respectively, for polyp detection. The polyp classification model attained an AUC of 0.9989 (95% CI: 0.9954-1.00). The external validation from 3 hospital results achieved 0.9516 (95% CI: 0.9295-0.9670) with the lesion-based sensitivity and a frame-based specificity of 0.9720 (95% CI: 0.9713-0.9726) for polyp detection. The model achieved an AUC of 0.9521 (95% CI: 0.9308-0.9734) for polyp classification. The high-performance, deep-learning-based system could be used in clinical practice to facilitate rapid, efficient and reliable decisions by physicians and endoscopists.

Retraction: New synthesis of 2-aroylbenzothiazoles via metal-free domino transformations of anilines, acetophenones, and elemental sulfur.

[This retracts the article DOI: 10.1039/D0RA01750G.].

Oxidative annulation of acetophenones and 2-aminobenzothiazoles catalyzed by reusable nickel-doped LaMnO3 perovskites.

Synthesis of imidazole[2,1-b]benzothiazoles often suffers from the use of pre-functionalized substrates and/or homogeneous, non-recyclable catalytic systems. Herein we report a method for direct coupling of acetophenones and 2-aminobenzothiazoles in the presence of reusable perovskites, namely LaMn0.95Ni0.05O3. Imidazole[2,1-b]benzothiazoles were obtained in moderate to good yields and contained an array of useful functionalities. Control experiments indicated that the perovskites played pivotal roles in halogenation and condensation steps.

Copper-promoted direct sulfenylation of C1-H bonds in 4-aryl pyrrolo[1,2-a]quinoxalines.

Methods for direct functionalization of C(sp2)-H bonds in pyrrolo[1,2-a]quinoxalines have witnessed emerging development over the last decade. Herein we report a new tactic to afford a selective sulfenylation of 4-aryl pyrrolo[1,2-a]quinoxalines with diaryl disulfides. The reactions proceeded in the presence of a copper catalyst and potassium iodide promoter. Functionalities including nitro, ester, amide, methylthio, and halogen groups were all tolerated. Our method offers a convenient route to obtain highly substituted pyrrolo[1,2-a]quinoxalines-based thioethers in moderate to good yields.

Multimodality imaging in the diagnosis of bioprosthetic aortic valve endocarditis: A case report.

Introduction: Prosthetic valve infective endocarditis (PVE) is a diagnostic challenge even in the era of multimodality cardiovascular imaging. Case presentation: The patient was a 67-year-old male with a three-year history of bioprosthetic aortic valve replacement who presented with persistent fever and negative blood cultures. The initial transthoracic echocardiography revealed a thickened aortic root. An abscess formation was visualized upon subsequent three-dimensional transesophageal echocardiography and positron emission tomography/computerized tomography (PET/CT). The patient underwent an urgent necrotic tissue debridement and a redo Bentall surgery. The real-time polymerase chain reaction of excised tissues was positive for Streptococcus. Clinical discussion: The diagnosis of PVE and its complications requires the integration of clinical, microbiological, and serial imaging data. Although advanced imaging modalities like PET/CT allow a timely diagnosis and management, their routine use in resource-limited scenarios is difficult. Conclusion: Multimodality cardiovascular imaging plays an important role in the diagnosis of PVE. Serial echocardiographic and clinical assessments are possible alternatives when the access to advanced cardiovascular imaging modalities is limited.

Self-Adaptive Dirhodium Complexes in a Metal-Organic Framework for Synthesis of N-H Aziridines.

Conformational dynamics of active sites in enzymes enable great control over the catalytic process. Herein, we constructed a metal-organic framework with conformationally dynamic active sites (Rh2-ZIF-8). The active sites in Rh2-ZIF-8 were composed of the imidazolate-bridged bimetallic center with a catalytic dirhodium moiety and structural zinc site. Even though the coordination sphere of the dirhodium species was saturated with two circularly arranged esp groups and two axial 2-MeIm ligands, it could still effectively catalyze the direct synthesis of N-H aziridines from olefins with high activity. We found that such a self-adaptive catalytic process was based on the dynamic breakage and reformation of the rhodium-zinc imidazolate bridges. Interestingly, the in situ generated dirhodium site with a unique Rh2(esp)2(2-MeIm)1 configuration was able to exhibit obviously enhanced selectivity compared to homogeneous catalyst Rh2(esp)2. Furthermore, the surrounding zinc imidazolate groups could effectively protect the dirhodium moieties from harsh environments, and this ultimately endowed it with high stability.

Crucial Role of Surfactants in Improving the Extraction Efficiency of ß-Amyrin from Dischidia major Using Ultrasound-Assisted Extraction Coupled with Gas Bubble Flotation.

Ultrasound-assisted extraction coupled with gas bubble flotation was developed as a green method for extracting ß-amyrin fromDischidia major. The solvent system was water:ethanol (9:1). To improve the adsorption of ß-amyrin onto the air/liquid interface during flotation, surfactants were employed; however, the positive effect was only observed with cationic surfactants. High-performance liquid chromatography with spectrophotometric detection (HPLC-PDA) was, for the first time, applied to quantify the ß-amyrin content in D. major and its extracts. With the assistance of surfactants, the foam layer collected from flotation showed high selectivity toward ß-amyrin. The product content was notably increased after surfactants had been removed from the solution.

Hot Isostatic Pressing for Fatigue Critical Additively Manufactured Ti-6Al-4V.

The efficacy of hot isostatic pressing (HIP) for enhancing fatigue performance is investigated for additively manufactured (AM) Ti-6Al-4V. The limitations of HIP are probed by varying the initial material state via the selection of AM system, powder chemical composition, and process parameters. We demonstrate that the fatigue performance of HIP'd AM Ti-6Al-4V depends on the as-built quality of the material. Differences in common material attributes, such as pre-HIP defect populations or post-HIP microstructure morphology, are shown to be insufficient to explain the observed discrepancies in performance. This implies that additional microstructure attributes or localized deviations from the expected structure control the failure of this material. Finally, HIP parameters outside ASTM recommendations were explored, where a reduced temperature and high-pressure treatment yielded significantly improved fatigue performance.

Prevalence of Tumor Genomic Alterations in Homologous Recombination Repair Genes Among Taiwanese Breast Cancers.

PURPOSE: Deleterious germline BRCA1/2 mutations are among the most highly pathogenic variants in hereditary breast and ovarian cancer syndrome. Recently, genes implicated in homologous recombination repair (HRR) pathways have been investigated extensively. Defective HRR genes may indicate potential clinical benefits from PARP (poly ADP ribose polymerase) inhibitors beyond BRCA1/2 mutations. METHODS: We evaluated the prevalence of BRCA1/2 mutations as well as alterations in HRR genes with targeted sequencing. A total of 648 consecutive breast cancer samples were assayed, and HRR genes were evaluated for prevalence in breast cancer tissues. RESULTS: Among 648 breast cancers, there were 17 truncating and 2 missense mutations in BRCA1 and 45 truncating and 1 missense mutation in BRCA2, impacting 3% and 5% of the study population (collectively altered in 6%) with cooccurrence of BRCA1/2 in 7 breast cancers. On the other hand, HRR genes were altered in 122 (19%) breast cancers, while TBB (Talazoparib Beyond BRCA) trial-interrogated genes (excluding BRCA1/2) were mutated in 107 (17%) patients. Beyond BRCA1/2, the most prevalent HRR mutant genes came from ARID1A (7%), PALB2 (7%), and PTEN (6%). Collectively, 164 (25%) of the 648 Taiwanese breast cancer samples harbored at least one mutation among HRR genes. CONCLUSIONS: The prevalence of BRCA1/2 mutations was far below one tenth, while the prevalence of HRR mutations was much higher and approached one-fourth among Taiwanese breast cancers. Further opportunities to take advantage of defective HRR genes for breast cancer treatment should be sought for the realization of precision medicine.

Metal-Free Annulation of 2-Nitrobenzyl Alcohols and Tetrahydroisoquinolines toward the Divergent Synthesis of Quinazolinones and Quinazolinethiones.

A simple metal-free method for the synthesis of quinazolinones from commercially available 2-nitrobenzyl alcohols and tetrahydroisoquinolines is developed. The reaction conditions were tolerant of an array of functionalities such as halogen, tertiary amine, protected alcohol, and ester groups. Under nearly identical conditions, quinazolinethiones were obtained in the presence of elemental sulfur and suitable mediators.

Vietnam Association of Gastroenterology (VNAGE) consensus on the management of Helicobacter pylori infection.

Helicobacter pylori (H. pylori) infection is prevalent and has a rapidly increasing antibiotic resistance rate in Vietnam. Reinfection is quite common, and gastric carcinoma remains one of the most common malignancies, which is not uncommon to develop after successful eradication. The purpose of this consensus is to provide updated recommendations on the management of H. pylori infection in the country. The consensus panel consisted of 32 experts from 14 major universities and institutions in Vietnam who were invited to review the evidence and develop the statements using the Delphi method. The process followed the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system. The consensus level was defined as ≥80% for agreement on the proposed statements. Due to the limited availability of high-quality local evidence, this consensus was also based on high-quality evidence from international studies, especially those conducted in other populations in the Asia-Pacific region. The panel finally reached a consensus on 27 statements after two voting rounds, which consisted of four sections (1) indications for testing and selection of diagnostic tests (2), treatment regimens, (3) post-treatment confirmation of H. pylori status, and (4) reinfection prevention methods and follow-up after eradication. Important issues that require further evidence include studies on third-line regimens, strategies to prevent H. pylori reinfection, and post-eradication follow-up for precancerous gastric lesions. We hope this consensus will help guide the current clinical practice in Vietnam and promote multicenter studies in the country and international collaborations.

Predicting Breast Cancer Gene Expression Signature by Applying Deep Convolutional Neural Networks From Unannotated Pathological Images.

We proposed a highly versatile two-step transfer learning pipeline for predicting the gene signature defining the intrinsic breast cancer subtypes using unannotated pathological images. Deciphering breast cancer molecular subtypes by deep learning approaches could provide a convenient and efficient method for the diagnosis of breast cancer patients. It could reduce costs associated with transcriptional profiling and subtyping discrepancy between IHC assays and mRNA expression. Four pretrained models such as VGG16, ResNet50, ResNet101, and Xception were trained with our in-house pathological images from breast cancer patient with recurrent status in the first transfer learning step and TCGA-BRCA dataset for the second transfer learning step. Furthermore, we also trained ResNet101 model with weight from ImageNet for comparison to the aforementioned models. The two-step deep learning models showed promising classification results of the four breast cancer intrinsic subtypes with accuracy ranging from 0.68 (ResNet50) to 0.78 (ResNet101) in both validation and testing sets. Additionally, the overall accuracy of slide-wise prediction showed even higher average accuracy of 0.913 with ResNet101 model. The micro- and macro-average area under the curve (AUC) for these models ranged from 0.88 (ResNet50) to 0.94 (ResNet101), whereas ResNet101_imgnet weighted with ImageNet archived an AUC of 0.92. We also show the deep learning model prediction performance is significantly improved relatively to the common Genefu tool for breast cancer classification. Our study demonstrated the capability of deep learning models to classify breast cancer intrinsic subtypes without the region of interest annotation, which will facilitate the clinical applicability of the proposed models.

Prognostic and Genomic Analysis of Proteasome 20S Subunit Alpha (PSMA) Family Members in Breast Cancer.

The complexity of breast cancer includes many interacting biological processes, and proteasome alpha (PSMA) subunits are reported to be involved in many cancerous diseases, although the transcriptomic expression of this gene family in breast cancer still needs to be more thoroughly investigated. Consequently, we used a holistic bioinformatics approach to study the PSMA genes involved in breast cancer by integrating several well-established high-throughput databases and tools, such as cBioPortal, Oncomine, and the Kaplan-Meier plotter. Additionally, correlations of breast cancer patient survival and PSMA messenger RNA expressions were also studied. The results demonstrated that breast cancer tissues had higher expression levels of PSMA genes compared to normal breast tissues. Furthermore, PSMA2, PSMA3, PSMA4, PSMA6, and PSMA7 showed high expression levels, which were correlated with poor survival of breast cancer patients. In contrast, PSMA5 and PSMA8 had high expression levels, which were associated with good prognoses. We also found that PSMA family genes were positively correlated with the cell cycle, ubiquinone metabolism, oxidative stress, and immune response signaling, including antigen presentation by major histocompatibility class, interferon-gamma, and the cluster of differentiation signaling. Collectively, these findings suggest that PSMA genes have the potential to serve as novel biomarkers and therapeutic targets for breast cancer. Nevertheless, the bioinformatic results from the present study would be strengthened with experimental validation in the future by prospective studies on the underlying biological mechanisms of PSMA genes and breast cancer.

Prediction of Breast Cancer Recurrence Using a Deep Convolutional Neural Network Without Region-of-Interest Labeling.

PURPOSE: The present study aimed to assign a risk score for breast cancer recurrence based on pathological whole slide images (WSIs) using a deep learning model. METHODS: A total of 233 WSIs from 138 breast cancer patients were assigned either a low-risk or a high-risk score based on a 70-gene signature. These images were processed into patches of 512x512 pixels by the PyHIST tool and underwent color normalization using the Macenko method. Afterward, out of focus and pixelated patches were removed using the Laplacian algorithm. Finally, the remaining patches (n=294,562) were split into 3 parts for model training (50%), validation (7%) and testing (43%). We used 6 pretrained models for transfer learning and evaluated their performance using accuracy, precision, recall, F1 score, confusion matrix, and AUC. Additionally, to demonstrate the robustness of the final model and its generalization capacity, the testing set was used for model evaluation. Finally, the GRAD-CAM algorithm was used for model visualization. RESULTS: Six models, namely VGG16, ResNet50, ResNet101, Inception_ResNet, EfficientB5, and Xception, achieved high performance in the validation set with an overall accuracy of 0.84, 0.85, 0.83, 0.84, 0.87, and 0.91, respectively. We selected Xception for assessment of the testing set, and this model achieved an overall accuracy of 0.87 with a patch-wise approach and 0.90 and 1.00 with a patient-wise approach for high-risk and low-risk groups, respectively. CONCLUSIONS: Our study demonstrated the feasibility and high performance of artificial intelligence models trained without region-of-interest labeling for predicting cancer recurrence based on a 70-gene signature risk score.