Anti-HER2 therapy response assessment for guiding treatment (de-)escalation in early HER2-positive breast cancer using a novel deep learning radiomics model.

OBJECTIVES: Anti-HER2 targeted therapy significantly reduces risk of relapse in HER2 + breast cancer. New measures are needed for a precise risk stratification to guide (de-)escalation of anti-HER2 strategy. METHODS: A total of 726 HER2 + cases who received no/single/dual anti-HER2 targeted therapies were split into three respective cohorts. A deep learning model (DeepTEPP) based on preoperative breast magnetic resonance (MR) was developed. Patients were scored and categorized into low-, moderate-, and high-risk groups. Recurrence-free survival (RFS) was compared in patients with different risk groups according to the anti-HER2 treatment they received, to validate the value of DeepTEPP in predicting treatment efficacy and guiding anti-HER2 strategy. RESULTS: DeepTEPP was capable of risk stratification and guiding anti-HER2 treatment strategy: DeepTEPP-Low patients (60.5%) did not derive significant RFS benefit from trastuzumab (p = 0.144), proposing an anti-HER2 de-escalation. DeepTEPP-Moderate patients (19.8%) significantly benefited from trastuzumab (p = 0.048), but did not obtain additional improvements from pertuzumab (p = 0.125). DeepTEPP-High patients (19.7%) significantly benefited from dual HER2 blockade (p = 0.045), suggesting an anti-HER2 escalation. CONCLUSIONS: DeepTEPP represents a pioneering MR-based deep learning model that enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thereby providing valuable guidance for anti-HER2 (de-)escalation strategies. DeepTEPP provides an important reference for choosing the appropriate individualized treatment in HER2 + breast cancer patients, warranting prospective validation. CLINICAL RELEVANCE STATEMENT: We built an MR-based deep learning model DeepTEPP, which enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thus guiding anti-HER2 (de-)escalation strategies in early HER2-positive breast cancer patients. KEY POINTS: • DeepTEPP is able to predict anti-HER2 effectiveness and to guide treatment (de-)escalation. • DeepTEPP demonstrated an impressive prognostic efficacy for recurrence-free survival and overall survival. • To our knowledge, this is one of the very few, also the largest study to test the efficacy of a deep learning model extracted from breast MR images on HER2-positive breast cancer survival and anti-HER2 therapy effectiveness prediction.

Histone deacetylase 3 deletion in alveolar type 2 epithelial cells prevents bleomycin-induced pulmonary fibrosis.

BACKGROUND: Epithelial mesenchymal transformation (EMT) in alveolar type 2 epithelial cells (AT2) is closely associated with pulmonary fibrosis (PF). Histone deacetylase 3 (HDAC3) is an important enzyme that regulates protein stability by modulating the acetylation level of non-histones. Here, we aimed to explore the potential role and regulatory mechanisms associated with HDAC3 in PF. METHODS: We quantified HDAC3 expression both in lung tissues from patients with PF and from bleomycin (BLM)-treated mice. HDAC3 was also detected in TGF-ß1-treated AT2. The mechanistic activity of HDAC3 in pulmonary fibrosis and EMT was also explored. RESULTS: HDAC3 was highly expressed in lung tissues from patients with PF and bleomycin (BLM)-treated mice, especially in AT2. Lung tissues from AT2-specific HDAC3-deficient mice stimulated with BLM showed alleviative fibrosis and EMT. Upstream of HDAC3, TGF-ß1/SMAD3 directly promoted HDAC3 transcription. Downstream of HDAC3, we also found that genetic or pharmacologic inhibition of HDAC3 inhibited GATA3 expression at the protein level rather than mRNA. Finally, we found that intraperitoneal administration of RGFP966, a selective inhibitor of HDAC3, could prevent mice from BLM-induced pulmonary fibrosis and EMT. CONCLUSION: TGF-ß1/SMAD3 directly promoted the transcription of HDAC3, which aggravated EMT in AT2 and pulmonary fibrosis in mice via deacetylation of GATA3 and inhibition of its degradation. Our results suggest that targeting HDAC3 in AT2 may provide a new therapeutic target for the prevention of PF.

OCIF: automatically learning the optimized clinical information fusion method for computer-aided diagnosis tasks.

PURPOSE: In computer-aided diagnosis, the fusion of image features extracted from neural networks and clinical information is crucial to improve diagnostic accuracy. How to integrate low-dimensional clinical information (LDCF) with high-dimensional network features (HDNF) is an urgent problem to be solved. We offer a new network search framework to address this problem, which can provide optimized LDCF fusion and efficient dimensionality reduction in HDNF. METHODS: OCIF innovatively uses Gaussian process optimization to explore the search space for the number of fully connected (FC) layers, the number of neurons in each FC layer, the activation function, the dropout factor, and whether to add clinical information to each FC layer. Moreover, OCIF employs transfer learning to reduce the training parameter space and improve search efficiency. To evaluate the effectiveness of the proposed OCIF, we utilized three popular end-to-end overall survival (OS) time prediction models to predict the three classes. RESULTS: Our experimental results show that applying OCIF to a classical computer-aided diagnosis neural network can improve classification accuracy. Experiments on the 2020 BRATS dataset prove that OCIF achieves satisfactory performance, with an accuracy of 0.684, precision of 0.735, recall of 0.684, and F1-score of 0.675 on the OS time prediction task. CONCLUSION: OCIF effectively and creatively combines clinical information and network features, leveraging both clinical information and image features to enhance the accuracy of the final diagnosis. Our experiments demonstrate that the use of OCIF can significantly improve computer-aided diagnosis accuracy, and the approach has the potential to be extended to other medical classification tasks as well.

Enhanced cycling stability of Li-sulfur battery composites by low pressure penetration of PEDOT:PSS coating.

A lithium sulfur composite electrode (SCPV) is prepared by in situ permeation of poly (3,4-dioxyethiophene):poly(styrene sulfonate) (PEDOT:PSS) with a thickness of about 10 nm onto the surface of a SC (sulfur and carbon nanotubes) electrode via a low pressure (3.3 kPa) method. The SCPV electrode exhibits a discharge capacity of 1320.0 mA h g-1, which is higher than that of the SC electrode (1265.8 mA h g-1) at 0.1C; furthermore, it exhibits a discharge capacity of 604.9 mA h g-1, which is almost twice that of the SC electrode (306.8 mA h g-1) at 2C, and it is due to the fact that PEDOT:PSS gel polymers store large amounts of electrolytes and have excellent electronic and ionic conductivities. However, the discharge capacity of a SCPV cathode remains at 91.87% after 200 cycles at 0.5C, which is more than twice that of the SC cathode (44.70%); this superior cycling stability is mainly due to the in situ fixation of PEDOT:PSS inside the SC electrode, which inhibits the shuttle effect and volume change during the cycling process, thus improving the cycling stability.

BP-Net: Boundary and perfusion feature guided dual-modality ultrasound video analysis network for fibrous cap integrity assessment.

Ultrasonography is one of the main imaging methods for monitoring and diagnosing atherosclerosis due to its non-invasiveness and low-cost. Automatic differentiation of carotid plaque fibrous cap integrity by using multi-modal ultrasound videos has significant diagnostic and prognostic value for cardiovascular and cerebrovascular disease patients. However, the task faces several challenges, including high variation in plaque location and shape, the absence of analysis mechanism focusing on fibrous cap, the lack of effective mechanism to capture the relevance among multi-modal data for feature fusion and selection, etc. To overcome these challenges, we propose a new target boundary and perfusion feature guided video analysis network (BP-Net) based on conventional B-mode ultrasound and contrast-enhanced ultrasound videos for assessing the integrity of fibrous cap. Based on our previously proposed plaque auto-tracking network, in our BP-Net, we further introduce the plaque edge attention module and reverse mechanism to focus the dual video analysis on the fiber cap of plaques. Moreover, to fully explore the rich information on the fibrous cap and inside/outside of the plaque, we propose a feature fusion module for B-mode and contrast video to filter out the most valuable features for fibrous cap integrity assessment. Finally, multi-head convolution attention is proposed and embedded into transformer-based network, which captures semantic features and global context information to obtain accurate evaluation of fibrous caps integrity. The experimental results demonstrate that the proposed method has high accuracy and generalizability with an accuracy of 92.35% and an AUC of 0.935, which outperforms than the state-of-the-art deep learning based methods. A series of comprehensive ablation studies suggest the effectiveness of each proposed component and show great potential in clinical application.

In Situ Fabrication of High Dielectric Constant Composite Films with Good Mechanical and Thermal Properties by Controlled Reduction.

As a common two-dimensional carbon material, graphene has been widely doped into polymers to prepare high-performance dielectric materials. However, the shortcomings of graphene, such as large specific surface area and poor dispersion, limit its further application. Therefore, in this work, to solve the problem regarding the uniform dispersion of graphene in the matrix, in situ polymerization was used to prepare graphene/polyimide films, in which 1,4-diiodobutane was used as a reduction agent to prevent the aggregation of graphene oxide (GO) during imidization. High dielectric constant composite films were obtained by adjusting the ratio of 1,4-diiodobutane in GO. The results show that the resulting graphene/polyimide composite film possessed a dielectric constant of up to 197.5, which was more than 58 times higher than that of the polyimide (PI) film. Furthermore, compared to the pure PI film, the composite films showed better thermal stability and mechanical properties. Thermal performance tests showed that the 1,4-diiodobutane added during the preparation of the composite film was thermally decomposed, and there was no residue. We believe our preparation method can be extended to other high dielectric composite films, which will facilitate their further development and application in high power density energy storage materials.

Lyophilized mRNA-lipid nanoparticle vaccines with long-term stability and high antigenicity against SARS-CoV-2.

Advanced mRNA vaccines play vital roles against SARS-CoV-2. However, most current mRNA delivery platforms need to be stored at -20 °C or -70 °C due to their poor stability, which severely restricts their availability. Herein, we develop a lyophilization technique to prepare SARS-CoV-2 mRNA-lipid nanoparticle vaccines with long-term thermostability. The physiochemical properties and bioactivities of lyophilized vaccines showed no change at 25 °C over 6 months, and the lyophilized SARS-CoV-2 mRNA vaccines could elicit potent humoral and cellular immunity whether in mice, rabbits, or rhesus macaques. Furthermore, in the human trial, administration of lyophilized Omicron mRNA vaccine as a booster shot also engendered strong immunity without severe adverse events, where the titers of neutralizing antibodies against Omicron BA.1/BA.2/BA.4 were increased by at least 253-fold after a booster shot following two doses of the commercial inactivated vaccine, CoronaVac. This lyophilization platform overcomes the instability of mRNA vaccines without affecting their bioactivity and significantly improves their accessibility, particularly in remote regions.

Deep learning and radiomics based automatic diagnosis of hippocampal sclerosis.

Accurate and rapid segmentation of the hippocampus can help doctors perform intractable temporal lobe epilepsy (TLE) preoperative evaluations to identify good surgical candidates. This study aims to establish a radiomics system for the automatic diagnosis of hippocampal sclerosis with the help of machine learning. A total of 240 cases were analysed to develop a diagnostic model. First, an automatic hippocampal segmentation process was established that exploits a priori knowledge of the relatively fixed location of the hippocampus in brain partitions, as well as a deep-learning segmentation network based on an Attention U-net. Then, we extracted 527 radiomics features from each side of the segmented hippocampus. The iterative sparse representation based on feature selection and a support vector machine classifier were finally used to establish the diagnostic model of hippocampal sclerosis. The diagnostic model consists of two consecutive steps: distinguish hippocampal sclerosis (HS) from normal control (NC) and detect whether the HS is located on the left or right side. When the automatic diagnosis model identified HS and NC, the sensitivity and specificity reached 0.941 and 0.917 in the 10-fold cross-validation set and 0.920 and 0.909 in the independent testing set. When the diagnostic model detected HS lateralization, the sensitivity and specificity reached 0.923 and 0.920 in cross-validation and 0.909 and 0.929 in independent testing. Our results show that the developed radiomics model can help detect TLE patients with hippocampal sclerosis and has the potential to simplify preoperative evaluations and select surgical candidates.

A universal strategy of glyconanoparticle preparation using a bifunctional linker for lectin sensing and cell imaging.

Glyconanoparticles (G-NPs), biofunctional nanomaterials that can fully combine the unique properties of nanoparticles (NPs) with the bioactivities of carbohydrates, have become an appealing nanoplatform in analytical chemistry and biomedical research. However, there is currently a lack of an efficient and universal method for facile immobilization of reducing carbohydrates on NPs while maintaining their structure integrity, greatly limiting the preparation and application of G-NPs. Herein, a new and universal strategy for preparing carbohydrate-functionalized gold nanoclusters (Au NCs) was developed by using S-(3-(methoxyamino)propyl) thioacetate (MPTA) as a new bifunctional linker. MPTA with an N-methoxyamine group (-NHOMe) and a thioacetyl group (-SAc) was synthesized by a two-step strategy and then grafted onto Au NCs by an efficient click reaction. Subsequently, reducing carbohydrates could be readily immobilized onto MPTA-functionalized Au NCs (MPTA-Au NCs) by a reducing end ring-closure reaction under mild conditions. The obtained G-NPs showed average size of 1.9 ± 0.42 nm and strong fluorescence at 610 nm. Carbohydrates grafted on G-NPs still retained their structure integrity and specific recognition ability toward their receptor proteins. Notably, the affinity between G-NPs and proteins was increased by 1300 times compared with free carbohydrates with an association constant of (1.47 ± 0.356) × 106 M-1. The prepared fluorescent G-NPs were also successfully applied to lectin sensing and targeted breast cancer cell imaging with good performance. These results indicated that the intact immobilization of reducing carbohydrates (whether naturally or chemically accessed) on NPs could be easily achieved using MPTA, providing a simple, efficient, and universal strategy for G-NP preparation.

Automatic multi-plaque tracking and segmentation in ultrasonic videos.

Carotid plaque tracking and segmentation in ultrasound videos is the premise for subsequent plaque property evaluation and treatment plan development. However, the task is quite challenging, as it needs to address the problems of poor image quality, plaque shape variations among frames, the existence of multiple plaques, etc. To overcome these challenges, we propose a new automatic multi-plaque tracking and segmentation (AMPTS) framework. AMPTS consists of three modules. The first module is a multi-object detector, in which a Dual Attention U-Net is proposed to detect multiple plaques and vessels simultaneously. The second module is a set of single-object trackers that can utilize the previous tracking results efficiently and achieve stable tracking of the current target by using channel attention and a ranking strategy. To make the first module and the second module work together, a parallel tracking module based on a simplified 'tracking-by-detection' mechanism is proposed to solve the challenge of tracking object variation. Extensive experiments are conducted to compare the proposed method with several state-of-the-art deep learning based methods. The experimental results demonstrate that the proposed method has high accuracy and generalizability with a Dice similarity coefficient of 0.83 which is 0.16, 0.06 and 0.27 greater than MAST (Lai et al., 2020), Track R-CNN (Voigtlaender et al., 2019) and VSD (Yang et al., 2019) respectively and has made significant improvements on seven other indicators. In the additional Testing set 2, our method achieved a Dice similarity coefficient of 0.80, an accuracy of 0.79, a precision of 0.91, a Recall 0.70, a F1 score of 0.79, an AP@0.5 of 0.92, an AP@0.7 of 0.74, and an expected average overlap of 0.79. Numerous ablation studies suggest the effectiveness of each proposed component and the great potential for multiple carotid plaques tracking and segmentation in clinical practice.

MIL normalization -- prerequisites for accurate MRI radiomics analysis.

The quality of magnetic resonance (MR) images obtained with different instruments and imaging parameters varies greatly. A large number of heterogeneous images are collected, and they suffer from acquisition variation. Such imaging quality differences will have a great impact on the radiomics analysis. The main differences in MR images include modality mismatch (M), intensity distribution variance (I), and layer-spacing differences (L), which are referred to as MIL differences in this paper for convenience. An MIL normalization system is proposed to reconstruct uneven MR images into high-quality data with complete modality, a uniform intensity distribution and consistent layer spacing. Three radiomics tasks, including tumor segmentation, pathological grading and genetic diagnosis of glioma, were used to verify the effect of MIL normalization on radiomics analysis. Three retrospective glioma datasets were analyzed in this study: BraTs (285 cases), TCGA (112 cases) and HuaShan (403 cases). They were used to test the effect of MIL on three different radiomics tasks, including tumor segmentation, pathological grading and genetic diagnosis. MIL normalization included three components: multimodal synthesis based on an encoder-decoder network, intensity normalization based on CycleGAN, and layer-spacing unification based on Statistical Parametric Mapping (SPM). The Dice similarity coefficient, areas under the curve (AUC) and six other indicators were calculated and compared after different normalization steps. The MIL normalization system can improved the Dice coefficient of segmentation by 9% (P < .001), the AUC of pathological grading by 32% (P < .001), and IDH1 status prediction by 25% (P < .001) when compared to non-normalization. The proposed MIL normalization system provides high-quality standardized data, which is a prerequisite for accurate radiomics analysis.

Automatic Detection of Gastric Wall Structure Based on Oral Contrast-Enhanced Ultrasound and Its Application on Tumor Screening.

Gastric cancer is the second most lethal type of malignant tumor in the world. Early diagnosis of gastric cancer can reduce the transformation to advanced cancer and improve the early treatment rate. As a cheap, real-time, non-invasive examination method, oral contrast-enhanced ultrasonography (OCUS) is a more acceptable way to diagnose gastric cancer than interventional diagnostic methods such as gastroscopy. In this paper, we proposed a new method for the diagnosis of gastric diseases by automatically analyzing the hierarchical structure of gastric wall in gastric ultrasound images, which is helpful to quantify the diagnosis information of gastric diseases and is a useful attempt for early screening of gastric cancer. We designed a gastric wall detection network based on U-net. On this basis, anisotropic diffusion technology was used to extract the layered structure of the gastric wall. A simple and useful gastric cancer screening model was obtained by calculating and counting the thickness of the five-layer structure of the gastric wall. The experimental results showed that our model can accurately identify the gastric wall, and it was found that the layered parameters of abnormal gastric wall is significantly different from that of normal gastric wall. For the screening of gastric disease, a statistical model based on gastric wall stratification can give a screening accuracy of 95% with AUC of 0.92.

Evaluation of lung involvement in COVID-19 pneumonia based on ultrasound images.

BACKGROUND: Lung ultrasound (LUS) can be an important imaging tool for the diagnosis and assessment of lung involvement. Ultrasound sonograms have been confirmed to illustrate damage to a person's lungs, which means that the correct classification and scoring of a patient's sonogram can be used to assess lung involvement. METHODS: The purpose of this study was to establish a lung involvement assessment model based on deep learning. A novel multimodal channel and receptive field attention network combined with ResNeXt (MCRFNet) was proposed to classify sonograms, and the network can automatically fuse shallow features and determine the importance of different channels and respective fields. Finally, sonogram classes were transformed into scores to evaluate lung involvement from the initial diagnosis to rehabilitation. RESULTS AND CONCLUSION: Using multicenter and multimodal ultrasound data from 104 patients, the diagnostic model achieved 94.39% accuracy, 82.28% precision, 76.27% sensitivity, and 96.44% specificity. The lung involvement severity and the trend of COVID-19 pneumonia were evaluated quantitatively.

Convergent Chemoenzymatic Strategy to Deliver a Diversity of Shigella flexneri Serotype-Specific O-Antigen Segments from a Unique Lightly Protected Tetrasaccharide Core.

Progress in glycoscience is strongly dependent on the availability of broadly diverse tailor-made, well-defined, and often complex oligosaccharides. Herein, going beyond natural resources and aiming to circumvent chemical boundaries in glycochemistry, we tackle the development of an in vitro chemoenzymatic strategy holding great potential to answer the need for molecular diversity characterizing microbial cell-surface carbohydrates. The concept is exemplified in the context of Shigella flexneri, a major cause of diarrhoeal disease. Aiming at a broad serotype coverage S. flexneri glycoconjugate vaccine, a non-natural lightly protected tetrasaccharide was designed for compatibility with (i) serotype-specific glucosylations and O-acetylations defining S. flexneri O-antigens, (ii) recognition by suitable α-transglucosylases, and (iii) programmed oligomerization following enzymatic α-d-glucosylation. The tetrasaccharide core was chemically synthesized from two crystalline monosaccharide precursors. Six α-transglucosylases found in the glycoside hydrolase family 70 were shown to transfer glucosyl residues on the non-natural acceptor. The successful proof of concept is achieved for a pentasaccharide featuring the glucosylation pattern from the S. flexneri type IV O-antigen. It demonstrates the potential of appropriately planned chemoenzymatic pathways involving non-natural acceptors and low-cost donor/transglucosylase systems to achieve the demanding regioselective α-d-glucosylation of large substrates, paving the way to microbial oligosaccharides of vaccinal interest.

A robust glycan labeling strategy using a new cationic hydrazide tag for MALDI-MS-based rapid and sensitive glycomics analysis.

Chemical derivatization of glycans is a common strategy to increase the analytical performance of MALDI-MS-based glycan profiling techniques. Hydrazide, one of the most popular tags, offers important advantages including allowing purification-free procedures. Several hydrazides have thus been used for glycomics combined with an on-target strategy to further simplify the analytical procedures. Usually, gentle heating and mildly acidic conditions with somewhat long reaction times are needed for these hydrazide derivatizations to reach a high reaction efficiency, which makes the current hydrazide tags not yet perfectly conducive to high-throughput analysis. To further optimize these hydrazide tags for high-throughput analysis, based on the structure of a reported hydrazide and the theoretical calculations, a new cationic hydrazide tag, 4-(hydrazinecarbonyl)-N,N,N-trimethylbenzenaminium (HTMBA), was designed, synthesized and tested in this work. HTMBA could completely derivatize glycans at room temperature in several seconds under very mildly acidic conditions (<3% acetic acid). A 19-fold enhancement in the signal intensity was obtained without interference from alkali adduct ions in the MALDI-MS detection of HTMBA-labeled maltoheptaose. To broaden the applicability of HTMBA, an HTMBA on-target derivatization (HOD) strategy was developed and fully validated with maltoheptaose and RNase B, and the method showed a good repeatability and stability. Finally, the HOD strategy was successfully applied to serum samples, 44 glycans in human serum were detected, and the O-acetylation information of sialic acid in horse serum was preserved. These results showed that the HOD strategy was suitable for the MS-based rapid analysis of all glycoforms in complex biological samples.

Carbohydrate-protein template synthesized high mannose loading gold nanoclusters: A powerful fluorescence probe for sensitive Concanavalin A detection and specific breast cancer cell imaging.

Protein-encapsulated gold nanoclusters (Au NCs) have recently gained much attention in biosensing and bioimaging applications owing to their remarkable fluorescence properties, nontoxicity and good biocompatibility. In this work, the mannose was grafted onto the bovine serum albumin (BSA) encapsulated Au NCs (BSA-Au NCs) to produce a mannose functionalized BSA-Au NCs (Man-BSA-Au NCs) as a new fluorescence probe for Concanavalin A (Con A) detection and human breast cancer cell imaging. A new strategy with mannose-BSA conjugates as template was firstly applied for the synthesis of Man-BSA-Au NCs, leading to a high loading of mannose (767.6 ± 7.2 mg/L) onto BSA-Au NCs. The as-prepared Man-BSA-Au NCs showed advantages of facile preparation, good monodispersity and strong red-emission. Notably, aggregation-induced fluorescence quenching of Man-BSA-Au NCs was triggered by Con A due to the multivalent cooperative interactions between mannose and Con A, which was subsequently confirmed by MALDI-TOF MS. Hence highly selective and sensitive fluorescence detection of Con A was achieved by using Man-BSA-Au NCs as a fluorescence sensor. A good linear relationship was obtained over the range of 0.01-1 µM (R2 = 0.994) with a detection limit of 0.62 nM (S/N = 3). The developed sensor was then applied to determine Con A in human serum with acceptable recoveries of 93.70-104.8%. Moreover, based on the specific recognition between mannose and overexpressed mannose receptors on human breast cancer cells, the Man-BSA-Au NCs were successfully utilized for cancer cell imaging with good specificity.

A type VI secretion system delivers a cell wall amidase to target bacterial competitors.

The human pathogen Pseudomonas aeruginosa harbors three paralogous zinc proteases annotated as AmpD, AmpDh2, and AmpDh3, which turn over the cell wall and cell wall-derived muropeptides. AmpD is cytoplasmic and plays a role in the recycling of cell wall muropeptides, with a link to antibiotic resistance. AmpDh2 is a periplasmic soluble enzyme with the former anchored to the inner leaflet of the outer membrane. We document, herein, that the type VI secretion system locus II (H2-T6SS) of P. aeruginosa delivers AmpDh3 (but not AmpD or AmpDh2) to the periplasm of a prey bacterium upon contact. AmpDh3 hydrolyzes the cell wall peptidoglycan of the prey bacterium, which leads to its killing, thereby providing a growth advantage for P. aeruginosa in bacterial competition. We also document that the periplasmic protein PA0808, heretofore of unknown function, affords self-protection from lysis by AmpDh3. Cognates of the AmpDh3-PA0808 pair are widely distributed across Gram-negative bacteria. Taken together, these findings underscore the importance of their function as an evolutionary advantage and that of the H2-T6SS as the means for the manifestation of the effect.

Efficient Iterative Synthesis of O-Acetylated Tri- to Pentadecasaccharides Related to the Lipopolysaccharide of Shigella flexneri Type 3 a through Di- and Trisaccharide Glycosyl Donors.

Protection against bacterial infections, including shigellosis, can be achieved by antibodies against the bacterial surface polysaccharide. In line with our efforts to develop vaccine candidates for shigellosis, we report herein the synthesis of penta-, deca-, and pentadecasaccharides as well as tri-, octa-, and tridecasaccharides as the endchain and intrachain fragments, respectively, of the surface polysaccharide of Shigella flexneri 3 a, a prevalent serotype. The syntheses relied on the efficiency of the trichloroacetimidate glycosylation chemistry, whereby iteration with di- and trisaccharide building blocks provided fragments made of up to three mono-O-acetylated polysaccharide repeating units. Pd(OH)2 -mediated hydrogenation/hydrogenolysis enabled the concomitant removal or conversion of up to 31 protecting groups of 4 different origins to provide the targets as propyl glycosides. Oligosaccharides comprising the octasaccharide segment were shown to display high conformational similarities in solution.

Fully resolved NMR correlation spectroscopy.

A new correlation experiment cited as "push-G-SERF" is reported. In the resulting phased 2D spectrum, the chemical shift information is selected along the direct dimension, whereas scalar couplings involving a selected proton nucleus are edited in the indirect domain. The robustness of this pulse sequence is demonstrated on compounds with increasing structural and spectral complexity, using state-of-the-art spectrometers. It allows for full resolution of both dimensions of the spectrum, yielding a straightforward assignment and measurement of the coupling network around a given proton in the molecule. This experiment is intended for chemists who want to address efficiently the structural analysis of molecules with an overcrowded spectrum.

Repairing the thiol-ene coupling reaction.

Thiol-ene coupling (TEC) reactions emerged as one of the most useful processes for coupling different molecular units under reaction mild conditions. However, TEC reactions involving weak CH bonds (allylic and benzylic fragments) are difficult to run and often low yielding. Mechanistic studies demonstrate that hydrogen-atom transfer processes at allylic and benzylic positions are responsible for the lack of efficiency of the radical-chain process. These competing reactions cannot be prevented, but reported herein is a method to repair the chain process by running the reaction in the presence of triethylborane and catechol. Under these reaction conditions, a unique repair mechanism leads to an efficient chain reaction, which is demonstrated with a broad range of anomeric O-allyl sugar derivatives including mono-, di-, and tetrasaccharides bearing various functionalities and protecting groups.