Deformation induced evolution of plasmonic responses in polymer grafted nanoparticle thin films.

Multi-functional nanoparticle thin films are being used in various applications ranging from biosensing to photo-voltaics. In this study, we integrate two different numerical approaches to understand the interplay between the mechanical deformation and optical response of polymer grafted plasmonic nanoparticle (PGPN) arrays. Using numerical simulations we examine the deformation of thin films formed by end-functionalised polymer grafted nanoparticles subject to uniaxial elongation. The induced deformation causes the particles in the thin film network to rearrange their positions by two different mechanisms viz. sliding and packing. In sliding, the particles move in the direction of induced deformation. On the other hand, in packing, the particles move in a direction normal to that of the induced deformation. By employing a Green's tensor formulation in polarizable backgrounds for evaluating the optical response of the nanoparticle network, we calculate the evolution of the plasmonic response of the structure as a function of strain. The results indicate that the evolution of plasmonic response closely follows the deformation. In particular, we show that the onset of relative electric field enhancement of the optical response occurs when there is significant rearrangement of the constituent PGPNs in the array. Furthermore, we show that depending on the local packing/sliding and the polarization of the incident light there can be both enhancement and suppression of the SERS response.

Smart plant disease net: Adaptive Dense Hybrid Convolution network with attention mechanism for IoT-based plant disease detection by improved optimization approach.

Plant diseases are rising nowadays. Plant diseases lead to high economic losses. Internet of Things (IoT) technology has found its application in various sectors. This led to the introduction of smart farming, in which IoT has been utilized to help identify the exact spot of the diseased affected region on the leaf from the vast farmland in a well-organized and automated manner. Thus, the main focus of this task is the introduction of a novel plant disease detection model that relies on IoT technology. The collected images are given to the Image Transmission phase. Here, the encryption task is performed by employing the Advanced Encryption Standard (AES) and also the decrypted plant images are fed to the pre-processing stage. The Mask Regions with Convolutional Neural Networks (R-CNN) are used to segment the pre-processed images. Then, the segmented images are given to the detection phase in which the Adaptive Dense Hybrid Convolution Network with Attention Mechanism (ADHCN-AM) approach is utilized to perform the detection of plant disease. From the ADHCN-AM, the final detected plant disease outcomes are obtained. Throughout the entire validation, the offered model shows 95% enhancement in terms of MCC showcasing its effectiveness over the existing approaches.

Characterizations of novel broad-spectrum lytic bacteriophages Sfin-2 and Sfin-6 infecting MDR Shigella spp. with their application on raw chicken to reduce the Shigella load.

The evidence and prevalence of multidrug-resistant (MDR) Shigella spp. poses a serious global threat to public health and the economy. Food- or water-borne MDR Shigella spp. demands an alternate strategy to counteract this threat. In this regard, phage therapy has garnered great interest from medical practitioners and researchers as a potential way to combat MDR pathogens. In this observation, we isolated Shigella phages from environmental water samples and tested against various clinically isolated MDR Shigella spp. In this study, we have defined the isolation and detailed physical and genomic characterizations of two phages Sfin-2 and Sfin-6 from environmental water samples. The phages exhibited potent lytic activity against Shigella flexneri, Shigella dysenteriae, and Shigella sonnei. They showed absorption within 5-10 min, a burst size ranging from ~74 to 265 PFU/cell, and a latent period of 5-20 min. The phages were stable at a broad pH range and survived an hour at 50°C. The purified phages Sfin-2 and Sfin-6 belong to the Siphoviridae family with an isometric head (64.90 ± 2.04 nm and 62.42 ± 4.04 nm, respectively) and a non-contractile tail (145 ± 8.5 nm and 148.47 ± 14.5 nm, respectively). The in silico analysis concluded that the size of the genomic DNA of the Sfin-2 phage is 50,390 bp with a GC content of 44.90%, while the genome size of the Sfin-6 phage is 50,523 bp with a GC content of 48.30%. A total of 85 and 83 putative open reading frames (ORFs) were predicted in the Sfin-2 and Sfin-6 phages, respectively. Furthermore, a comparative genomic and phylogenetic analysis revealed that both phages represented different isolates and novel members of the T1-like phages. Sfin-2 and Sfin-6 phages, either individually or in a cocktail form, showed a significant reduction in the viable Shigella count on raw chicken samples after 72 h of incubation. Therefore, these results indicate that these phages might have a potential role in therapeutic approaches designed for shigellosis patients as well as in the biological control of MDR Shigella spp. in the poultry or food industry during the course of meat storage.

Unveiling the molecular identity of the diminutive cyprinid, Horadandia brittani (Teleostei: Cyprinidae), a species endemic to Southern India.

BACKGROUND: Horadandia brittani is a small cyprinid fish species initially discovered in the coastal floodplains of southern India. For almost 50 years, the genus Horadandia was monotypic with a single species confined to Sri Lanka. In 1992, a new species H. brittani was described from south-western India. Despite being described as a separate species, H. brittani was later considered a synonym of H. atukorali, but in 2013, researchers recognized it as a distinct species based on morphological differences. Despite this clarification, there was still a need to validate the identity of H. brittani and determine its evolutionary relationship with its closely related species using DNA sequences. METHODS: To address the uncertainties surrounding the identity of H. brittani, the present study utilized molecular techniques to generate DNA sequences. Sample collection involved obtaining specimens of H. brittani from their natural habitats. Subsequently, DNA was extracted from the collected samples, and the mitochondrial cytochrome C oxidase (COI) gene was amplified using appropriate methods. RESULTS: The analysis of DNA sequences obtained from the COI gene revealed significant genetic distinctions between H. brittani and H. atukorali. The genetic distance values between these two species ranged from 3.21 to 3.63%, clearly indicating that these two species are genetically separate entities. The study successfully established the phylogenetic relationships between H. brittani and H. atukorali based on the COI gene sequences, further confirming the validity of H. brittani as a distinct and separate species. CONCLUSION: The findings of this study conclusively demonstrate that H. brittani is a valid and separate species, distinct from H. atukorali. The genetic analysis based on mitochondrial COI gene sequences provided strong evidence for the differentiation between these two species. The molecular data generated in this research can be used to identify H. brittani quickly and accurately in the future.

A Retrospective Analysis to Assess the Reasons for the Failure of Dental Implants.

Background: To assess the reasons for dental implant failures. Materials and Methods: A total of 100 implants were studied in 80 subjects. The radiographic investigations were performed to assess the record length of implant and diameter of implant. Risk factors, such as habit of smoking, history of diabetes, and hypertension, were recorded. The data were collected and assessed using Statistical Package for the Social Sciences (SPSS) software. The one-way analysis of variance (ANOVA) test was used to assess the failure rate in dental implants. A P-value less than 0.05 was considered statistically significant. Results: A total of 100 dental implants were included. The maximum dental implant failure was seen with a diameter <3.75 mm (30%) followed by 3.75-4.5 mm (16%) and >4.5 mm (6.15%). The difference was found to be significant (P < 0.05). Conclusion: Dental implant failure was high in dental implants with <3.75 mm diameter, dental implants with a length <10.0 mm, and among smokers.

Securing health care data through blockchain enabled collaborative machine learning.

Transferring of data in machine learning from one party to another party is one of the issues that has been in existence since the development of technology. Health care data collection using machine learning techniques can lead to privacy issues which cause disturbances among the parties and reduces the possibility to work with either of the parties. Since centralized way of information transfer between two parties can be limited and risky as they are connected using machine learning, this factor motivated us to use the decentralized way where there is no connection but model transfer between both parties will be in process through a federated way. The purpose of this research is to investigate a model transfer between a user and the client(s) in an organization using federated learning techniques and reward the client(s) for their efforts with tokens accordingly using blockchain technology. In this research, the user shares a model to organizations that are willing to volunteer their service to provide help to the user. The model is trained and transferred among the user and the clients in the organizations in a privacy preserving way. In this research, we found that the process of model transfer between user and the volunteered organizations works completely fine with the help of federated learning techniques and the client(s) is/are rewarded with tokens for their efforts. We used the COVID-19 dataset to test the federation process, which yielded individual results of 88% for contributor a, 85% for contributor b, and 74% for contributor c. When using the FedAvg algorithm, we were able to achieve a total accuracy of 82%.

Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images.

Rice (Oryza sativa) is India's major crop. India has the most land dedicated to rice agriculture, which includes both brown and white rice. Rice cultivation creates jobs and contributes significantly to the stability of the gross domestic product (GDP). Recognizing infection or disease using plant images is a hot study topic in agriculture and the modern computer era. This study paper provides an overview of numerous methodologies and analyses key characteristics of various classifiers and strategies used to detect rice illnesses. Papers from the last decade are thoroughly examined, covering studies on several rice plant diseases, and a survey based on essential aspects is presented. The survey aims to differentiate between approaches based on the classifier utilized. The survey provides information on the many strategies used to identify rice plant disease. Furthermore, model for detecting rice disease using enhanced convolutional neural network (CNN) is proposed. Deep neural networks have had a lot of success with picture categorization challenges. We show how deep neural networks may be utilized for plant disease recognition in the context of image classification in this research. Finally, this paper compares the existing approaches based on their accuracy.

Persistent bacteremia predicts poor outcomes among neutropenic patients with carbapenem-resistant gram-negative bloodstream infections receiving appropriate therapy.

PURPOSE: Identifying persistent bacteremia early in patients with neutropenia may improve outcome. This study evaluated the role of follow-up blood cultures (FUBC) positivity in predicting outcomes among patients with neutropenia and carbapenem-resistant gram-negative bloodstream infections (CRGNBSI). METHODS: This retrospective cohort study conducted between December 2017 and April 2022 included patients more than 15 years old with neutropenia and CRGNBSI, who survived for ≥ 48 h, receiving appropriate antibiotic therapy and had FUBCs. Patients with polymicrobial bacteremia within 30 days were excluded. The primary outcome was 30 day mortality. Persistent bacteremia, septic shock, recovery from neutropenia, prolonged or profound neutropenia, requirement of intensive care and dialysis, and initiation of appropriate empirical therapy were also studied. RESULTS: In our study cohort of 155 patients, the 30 day mortality rate was 47.7%. Persistent bacteremia was common in our patient cohort (43.8%). Carbapenem resistant isolates identified in the study were K.pneumoniae (80%), E.coli (12.26%), P.aeruginosa (5.16%), A.baumanii (1.94%) and E.cloacae (0.65%). The median time for sending a FUBC was 2 days (IQR, 1-3 days). Patients with persistent bacteremia had higher mortality than those without (56.76% versus 32.1%; p < 0.001). Appropriate initial empirical therapy was given to 70.9%. Recovery from neutropenia occurred in 57.4% while 25.8% had prolonged or profound neutropenia. Sixty-nine percent (107/155) had septic shock and needed intensive care; 12.2% of patients required dialysis. Non-recovery from neutropenia (aHR, 4.28; 95% CI 2.53-7.23), presence of septic shock (aHR, 4.42; 95%CI 1.47-13.28), requirement of intensive care (aHR,3.12;95%CI 1.23-7.93), and persistent bacteremia (aHR,1.74; 95%CI 1.05-2.89) significantly predicted poor outcomes in multivariable analysis. CONCLUSION: FUBC showing persistent bacteremia predicted poor outcomes among neutropenic patients with carbapenem-resistant gram-negative bloodstream infections (CRGNBSI) and should be routinely reported.

Evaluating web accessibility of educational institutions websites using a variable magnitude approach.

The World Wide Web serves as an excellent platform for information dissemination. Educational institutions such as universities are utilizing the web medium to reach their target audience. In the post-Covid-19 scenario, the web medium has obtained increased significance as it has become the primary access channel to reach these institutions. In this backdrop, it becomes essential to analyze the accessibility of these sites for students with special needs. This paper presents an approach to compute the accessibility of web pages for persons with disabilities. A variable magnitude approach is proposed in this paper for the computation of accessibility barrier count as a combination of two different components. The proposed approach is experimented with top ranked higher educational institution websites of India. Based on the inferences from the results and inputs received from students with disabilities, a set of suggestions have been compiled by this paper to minimize the barriers faced by persons with disabilities in consuming these web resources.

Prediction and Prevention of Gestational Diabetes Mellitus and Its Sequelae by Administering Metformin in the Early Weeks of Pregnancy.

Diabetes mellitus in recent years has become a relentlessly evolving pandemic. Measures for the screening and early detection of diabetes are practiced all around the world. However, considering the ever-increasing magnitude of the problem, the present efforts should especially focus on the primordial prevention of diabetes. A ray of hope for preventing the development of diabetes in an individual arises from the concept that many adult-onset diseases have already been programmed while the individual was still in-utero. In women with hyperglycemia-in-pregnancy, maternal hyperglycemia results in fetal hyperinsulinemia, which leads to increased adiposity in the fetus, and insulin resistance and diabetes in adulthood. We have ventured to point out that the fetal beta-cells start secreting insulin at 10-11 weeks of pregnancy and fetal hyperinsulinemia persists with maternal hyperglycemia, in a pregnant woman who would develop gestational diabetes. Considering the fetal glucose-steal phenomenon and the fetal renal threshold for glucose, we have suggested a two-hour post-prandial blood-glucose (PPBG) value of >110 mg/dL as the cut-off for the prediction of gestational diabetes in the early weeks of pregnancy. Furthermore, we have emphasized the use of metformin in addition to medical nutrition therapy in the early weeks to maintain PPBG around 110 mg/dL to prevent gestational diabetes. In this paper, we recommend early, universal screening of all pregnant women during the early weeks of the first trimester and put forward that a two-hour PPBG of >110 mg/dl during the 8th-10th week of pregnancy would predict the risk of gestational diabetes in the pregnant woman. We suggest early testing and intervention to prevent the development of fetal hyperinsulinemia as a primordial prevention approach for diabetes.

Mechanical response of networks formed by end-functionalised spherical polymer grafted nanoparticles.

Via computer simulations we examine the mechanical response of hybrid polymer-particle networks composed of rigid spherical nanoparticles with long flexible polymer chains grafted onto their surface. The canopy of grafted polymer arms are end-functionalised such that interacting polymer-grafted nanoparticles (PGNs) form labile bonds when their coronas overlap. In the present study, the number of grafted arms, f, are such that the PGN brushes are in the small (f = 600) and intermediate curvature (f = 900 and 1200) regime with stable bonded interactions. To investigate the mechanical response of networks formed by these PGNs, controlled uniaxial elongation at a specified pulling rate is imposed on a 2-D network of PGNs placed on a hexagonal lattice. In the simulations, the force required to deform the network is measured as a function of the elongation and pulling rate imposed on the network until the network fails. By analysis of the force-strain curves and the rearrangement of the PGNs in the network we show that an increase in the number of grafted arms, pulling velocity and energy of the bonded interactions alters both the toughness and the mode of failure of the networks. In particular, we show that an increase in the number of grafted arms results in a reduction of toughness. Furthermore, analysis of the simulations of force relaxation after rapid extension indicates that the relaxation in deformed networks can be characterised by one or two time scales that depend on the number of grafted arms. The analysis of force-strain curves and force relaxation demonstrate the role of Deborah number, De, and the limitations in the use of a unique De in understanding the mechanical response of the networks respectively.

Are general circulation models obsolete?

Traditional general circulation models, or GCMs-that is, three-dimensional dynamical models with unresolved terms represented in equations with tunable parameters-have been a mainstay of climate research for several decades, and some of the pioneering studies have recently been recognized by a Nobel prize in Physics. Yet, there is considerable debate around their continuing role in the future. Frequently mentioned as limitations of GCMs are the structural error and uncertainty across models with different representations of unresolved scales and the fact that the models are tuned to reproduce certain aspects of the observed Earth. We consider these shortcomings in the context of a future generation of models that may address these issues through substantially higher resolution and detail, or through the use of machine learning techniques to match them better to observations, theory, and process models. It is our contention that calibration, far from being a weakness of models, is an essential element in the simulation of complex systems, and contributes to our understanding of their inner workings. Models can be calibrated to reveal both fine-scale detail and the global response to external perturbations. New methods enable us to articulate and improve the connections between the different levels of abstract representation of climate processes, and our understanding resides in an entire hierarchy of models where GCMs will continue to play a central role for the foreseeable future.

Pulmonary nocardiosis: Risk factors and species distribution from a high burden centre.

Nocardiosis is a clinical and diagnostic challenge. This was a retrospective study carried out on cases of pulmonary nocardiosis presenting over 15 years. Clinical data was retrieved using the electronic patient records. Vitek MS 3.2 (MALDI TOF MS) was carried out on 22 isolates and sequencing on another 9 isolates. Of 71 patients presenting with pulmonary nocardiosis, 58 (81.6%) were on immunosuppressant therapy, 26 (46%) had a previous lung pathology, 11 (8%) were HIV associated. Disseminated disease was seen in 6 (8.4%). There were 8 (11.26%) deaths in this cohort of patients. Of 31/71 identified to species, the most common were Nocardia cyriacigeorgica (n â€‹= â€‹11) followed by Nocardia farcinica (n â€‹= â€‹9).

Thermal stabilization of diverse biologics using reversible hydrogels.

Improving the thermal stability of biologics, including vaccines, is critical to reduce the economic costs and health risks associated with the cold chain. Here, we designed a versatile, safe, and easy-to-use reversible PEG-based hydrogel platform formed via dynamic covalent boronic ester cross-linking for the encapsulation, stabilization, and on-demand release of biologics. Using these reversible hydrogels, we thermally stabilized a wide range of biologics up to 65°C, including model enzymes, heat-sensitive clinical diagnostic enzymes (DNA gyrase and topoisomerase I), protein-based vaccines (H5N1 hemagglutinin), and whole viruses (adenovirus type 5). Our data support a generalized protection mechanism for the thermal stabilization of diverse biologics using direct encapsulation in reversible hydrogels. Furthermore, preliminary toxicology data suggest that the components of our hydrogel are safe for in vivo use. Our reversible hydrogel platform offers a simple material solution to mitigate the costs and risks associated with reliance on a continuous cold chain for biologic transport and storage.

Effect of functional anisotropy on the local dynamics of polymer grafted nanoparticles.

End-functionalised polymer grafted nanoparticles (PGNs) form bonds when their coronas overlap. The bonded interactions between the overlapping PGNs depend on the energy of the bonds (U). In the present study, oscillatory deformation imposed on a simple system with interacting PGNs placed on the vertices of a triangle is employed to examine the local dynamics as a function of energy of the bonds and the frequency of oscillation relative to the characteristic rupture frequency, ω0 = 2πν exp(-U/kBT), of the bonds. In particular, the effect of functional anisotropy is studied by introducing bonds of two different energies between adjacent PGNs. A multicomponent model developed by Kadre and Iyer, Macromol. Theory Simul., 2021, 30, 2100005, that combines the features of effective interactions between PGNs, self-consistent field theory and master equation approach to study bond kinetics is employed to obtain the local dynamics. The resulting force-strain curves are found to exhibit a simple broken symmetry where Fx (γ,) ≠ -Fx (-γ,-) and Fy (γ,) ≠ Fy (-γ,-) in systems with functional anisotropy. Fourier analysis of the dynamic response reveals that functional anisotropy leads to finite even harmonic terms and systematic variation of both the elastic and dissipative response from that of the isotropic systems. Furthermore, the intra-cycle variations in the strain stiffening and shear thickening ratios obtained from the analysis indicate that functional anisotropy leads to anisotropic nonlinear response.

Stress Relaxation and Composition of Hydrazone-Crosslinked Hybrid Biopolymer-Synthetic Hydrogels Determine Spreading and Secretory Properties of MSCs.

The extracellular matrix plays a critical role in mechanosensing and thereby influences the secretory properties of bone-marrow-derived mesenchymal stem/stromal cells (MSCs). As a result, interest has grown in the development of biomaterials with tunable properties for the expansion and delivery of MSCs that are used in cell-based therapies. Herein, stress-relaxing hydrogels are synthesized as hybrid networks containing both biopolymer and synthetic macromer components. Hyaluronic acid is functionalized with either aldehyde or hydrazide groups to form covalent adaptable hydrazone networks, which are stabilized by poly(ethylene glycol) functionalized with bicyclononyne and heterobifunctional small molecule crosslinkers containing azide and benzaldehyde moieties. Tuning the composition of these gels allows for controlled variation in the characteristic timescale for stress relaxation and the amount of stress relaxed. Over this compositional space, MSCs are observed to spread in formulations with higher degrees of adaptability, with aspect ratios of 1.60 ± 0.18, and YAP nuclear:cytoplasm ratios of 6.5 ± 1.3. Finally, a maximum MSC pericellular protein thickness of 1.45 ± 0.38 µm occurred in highly stress-relaxing gels, compared to 1.05 ± 0.25 µm in non-adaptable controls. Collectively, this study contributes a new understanding of the role of compositionally defined stress relaxation on MSCs mechanosensing and secretion.

Nasopharyngeal colonization of otopathogens in South Indian children with acute otitis media - A case control pilot study.

BACKGROUND: Acute otitis media (AOM) is an inflammatory disease of the middle ear causing significant morbidity in early childhood. A pilot study was undertaken to identify the role of various risk factors South Indian children with AOM, especially the role of nasopharyngeal otopathogens. METHODOLOGY: A prospective case control pilot study was conducted in children aged below six years, presenting to a single tertiary care from 2018 to 2019. Fifty cases with AOM and 45 age and gender matched controls were recruited. Two nasopharyngeal swabs were collected, one was processed for bacterial culture. The other swab was processed according to the CDC recommended broth enrichment method to identify carriage of S. pneumoniae. Subsequent serotyping was done by Quellung method and conventional sequential multiplex PCR. RESULT: Otalgia was the major presentation seen in 92% of the children with AOM. None of the clinical and demographic characteristics were found to be statistically significant between the cases and controls. The most common otopathogen was S. pneumoniae (55%) followed by H. influenza (29%). The common S. pneumoniae serotypes encountered were 11A and 19F.Nasopharyngeal colonization with S. pneumoniae [OR 6.57, p < 0.003] and H. influenzae [OR14.18, p < 0.003] were significant risk factors for AOM in children. The risk increased with co-colonization (OR 13.89,p < 0.003). CONCLUSION: This study strengthens the significant association between nasopharyngeal colonization of otopathogens and AOM as a risk factor that is enhanced by co-colonization.S. pneumoniae was the main otopathogen in this population, serotypes 11A and 19F being the most common.

Further Developments and Applications of Oxazoline-Containing Ligands in Asymmetric Catalysis.

The chiral oxazoline motif is present in many ligands that have been extensively applied in a series of important metal-catalyzed enantioselective reactions. This Review aims to provide a comprehensive overview of the most significant applications of oxazoline-containing ligands reported in the literature starting from 2009 until the end of 2018. The ligands are classified not by the reaction to which their metal complexes have been applied but by the nature of the denticity, chirality, and donor atoms involved. As a result, the continued development of ligand architectural design from mono(oxazolines), to bis(oxazolines), to tris(oxazolines) and tetra(oxazolines) and variations thereof can be more easily monitored by the reader. In addition, the key transition states of selected asymmetric transformations will be given to illustrate the features that give rise to high levels of asymmetric induction. As a further aid to the reader, we summarize the majority of schemes with representative examples that highlight the variation in % yields and % ees for carefully selected substrates. This Review should be of particular interest to the experts in the field but also serve as a useful starting point to new researchers in this area. It is hoped that this Review will stimulate both the development/design of new ligands and their applications in novel metal-catalyzed asymmetric transformations.

Climbing down Charney's ladder: machine learning and the post-Dennard era of computational climate science.

The advent of digital computing in the 1950s sparked a revolution in the science of weather and climate. Meteorology, long based on extrapolating patterns in space and time, gave way to computational methods in a decade of advances in numerical weather forecasting. Those same methods also gave rise to computational climate science, studying the behaviour of those same numerical equations over intervals much longer than weather events, and changes in external boundary conditions. Several subsequent decades of exponential growth in computational power have brought us to the present day, where models ever grow in resolution and complexity, capable of mastery of many small-scale phenomena with global repercussions, and ever more intricate feedbacks in the Earth system. The current juncture in computing, seven decades later, heralds an end to what is called Dennard scaling, the physics behind ever smaller computational units and ever faster arithmetic. This is prompting a fundamental change in our approach to the simulation of weather and climate, potentially as revolutionary as that wrought by John von Neumann in the 1950s. One approach could return us to an earlier era of pattern recognition and extrapolation, this time aided by computational power. Another approach could lead us to insights that continue to be expressed in mathematical equations. In either approach, or any synthesis of those, it is clearly no longer the steady march of the last few decades, continuing to add detail to ever more elaborate models. In this prospectus, we attempt to show the outlines of how this may unfold in the coming decades, a new harnessing of physical knowledge, computation and data. This article is part of the theme issue 'Machine learning for weather and climate modelling'.

Synthesis of α-Aryl Oxindoles by Friedel-Crafts Alkylation of Arenes.

α-Aryl oxindoles are accessed from isatin via a two-step procedure involving a phospha-Brook rearrangement and a Friedel-Crafts alkylation in a one-pot procedure. The use of 1,1,1,3,3,3-hexafluoro-2-propanol as solvent significantly extended the reaction substrate scope to include relatively less electron-rich arenes including benzene. This new alkylation method is fast and straightforward and allows for the direct introduction of the oxindole moiety onto a range of aromatic compounds including phenols. Additionally, the application of arylated products was shown in decarboxylative asymmetric allylation and protonation.