Palladium(I)-Iodide-Catalyzed Deoxygenative Heck Reaction of Vinyl Triflates: A Formate-Mediated Cross-Electrophile Reductive Coupling with cine-Substitution.

The first deoxygenative Heck reactions are described, as illustrated by formate-mediated cine-substitutions of vinyl triflates with aryl iodides. The collective data corroborate a mechanism in which Pd(OAc)2 and Bu4NI form the dianionic iodide-bridged dimer [Pd2I6][NBu4]2, which, under reducing conditions, serves as a precursor to the palladium(I) complex [Pd2I4][NBu4]2. Dinculear oxidative addition of aryl iodide forms [Pd2I5(Ar)][NBu4]2, which dissociates to the monometallic complex [PdI2(Ar)][NBu4]. Vinyl triflate migratory insertion-sulfonate elimination delivers a palladium(IV) carbene, which upon ß-hydride elimination/C-H reductive elimination gives the product of cine-substitution. These processes are the first efficient formate-mediated cross-electrophile reductive couplings beyond carbonyl addition.

Dual Ruthenium-Catalyzed Alkene Isomerization-Hydrogen Auto-Transfer Unlocks Skipped Dienes as Pronucleophiles for Enantioselective Alcohol C-H Allylation.

The first use of 1,4-pentadiene and 1,5-hexadiene as allylmetal pronucleophiles in regio-, anti-diastereo-, and enantioselective carbonyl addition from alcohol proelectrophiles is described. As corroborated by deuterium labeling experiments, primary alcohol dehydrogenation delivers a ruthenium hydride that affects alkene isomerization to furnish a conjugated diene, followed by transfer hydrogenative carbonyl addition. Hydrometalation appears to be assisted by the formation of a fluxional olefin-chelated homoallylic alkylruthenium complex II, which exists in equilibrium with its pentacoordinate η1 form to enable ß-hydride elimination. This effect confers remarkable chemoselectivity: while 1,4-pentadiene and 1,5-hexadiene are competent pronucleophiles, higher 1,n-dienes are not, and the olefinic functional groups of the products remain intact under conditions in which the 1,4- and 1,5-dienes isomerize. A survey of halide counterions reveals iodide-bound ruthenium-JOSIPHOS catalysts are uniquely effective in these processes. This method was used to prepare a previously reported C1-C7 substructure of (-)-pironetin in 4 vs 12 steps.

Historical perspective on ruthenium-catalyzed hydrogen transfer and survey of enantioselective hydrogen auto-transfer processes for the conversion of lower alcohols to higher alcohols.

Ruthenium-catalyzed hydrogen auto-transfer reactions for the direct enantioselective conversion of lower alcohols to higher alcohols are surveyed. These processes enable completely atom-efficient carbonyl addition from alcohol proelectrophiles in the absence of premetalated reagents or metallic reductants. Applications in target-oriented synthesis are highlighted, and a brief historical perspective on ruthenium-catalyzed hydrogen transfer processes is given.

Stereo- and Site-Selective Conversion of Primary Alcohols to Allylic Alcohols via Ruthenium-Catalyzed Hydrogen Auto-Transfer Mediated by 2-Butyne.

The first enantioselective ruthenium-catalyzed carbonyl vinylations via hydrogen autotransfer are described. Using a ruthenium-JOSIPHOS catalyst, primary alcohols 2a-2m and 2-butyne 1a are converted to chiral allylic alcohols 3a-3m with excellent levels of absolute stereocontrol. Notably, 1°,2°-1,3-diols participate in site-selective C-C coupling, enabling asymmetric carbonyl vinylation beyond premetalated reagents, exogenous reductants, or hydroxyl protecting groups. Using 2-propanol as a reductant, aldehydes dehydro-2a, 2l participate in highly enantioselective 2-butyne-mediated vinylation under otherwise identical reaction conditions. Regio-, stereo-, and site-selective vinylations mediated by 2-pentyne 1b to form adducts 3n, 3o, and epi-3o also are described. The tiglyl alcohol motif obtained upon butyne-mediated vinylation, which is itself found in diverse secondary metabolites, may be converted to commonly encountered polyketide stereodiads, -triads, and -tetrads, as demonstrated by the formation of adducts 4a-4d. The collective mechanistic studies, including deuterium labeling experiments, corroborate a catalytic cycle involving alcohol dehydrogenation to form a transient aldehyde and a ruthenium hydride, which engages in alkyne hydrometalation to form a nucleophilic vinylruthenium species that enacts carbonyl addition. A stereochemical model for carbonyl addition invoking formyl CH···I[Ru] and CH···O≡C[Ru] hydrogen bonds is proposed based on prior calculations and crystallographic data.

Combined thoraco-laparoscopic surgery for neonatal alimentary tract duplication.

Transdiaphragmatic duplication of digestive tract in infants is very rare. Open surgery is the main surgical treatment. We report the clinical data of a child with gastrointestinal duplication across the diaphragm who underwent thoraco-laparoscopic surgery at 12 days were retrospectively analyzed, and to explore the experience of thoraco-laparoscopic surgery in the treatment of alimentary tract duplication across the diaphragm in children, we believe minimally invasive surgical resection using thoracolaparoscopy is safe, effective and clinically feasible, focusing on the skilled use of the technique to achieve the ideal surgical effect and appearance.

Screening, purification and characterization of lipase from Burkholderia pyrrocinia B1213.

A lipase producing strain B1213 isolated from soil was identified as Burkholderia pyrrocinia based on 16S rRNA gene and recA sequeence analysis, making this the first report on the presence of a lipase from B. pyrrocinia. Under an aqueous two-phase purification strategy, which included (ATPE)-ion-exchange chromatography (IEC)-gel and filtration chromatography (GFC), the specific activity of the 35-kDa lipase was determined to be 875.7 U/mg protein. The optimum pH and temperature of this lipase was pH 8.0 and 50 °C, respectively. The lipase retained > 85% activity in isopropanol and acetone at 30 °C for 10 min but the activity was reduced to 10.6% in n-hexane. Mg2+, Al3+, Mn2+, and Fe3+ enhanced lipase activity at both 1 mM and 5 mM concentrations. p-NPP, a long-chain acyl group 4-NP ester, appeared to be a good substrate candidate.

Copper nitrate: a privileged reagent for organic synthesis.

Copper has been explored as an ideal candidate for replacing noble metals in organic synthesis, especially for practical large scale preparation. Recent decades have witnessed the renaissance and improvement of copper-catalyzed and copper-mediated organic reactions. Copper nitrate is a common inorganic copper salt which has been proved to be a ubiquitous reactant in organic synthesis due to its commercial availability, stability, inexpensiveness and environmentally benign nature. Copper nitrate could be used as a nitration reagent, oxidant, catalyst or promoter, and Lewis acid as well. Remarkably, great attention has been devoted to the efficient transformation of copper nitrate into functionalized or complicated compounds through various reaction types including cyclization, C-H activation, difunctionalization, nitration, rearrangement and asymmetric synthesis with chiral ligands. Further modification of copper nitrate, such as solid-supported copper nitrate or copper nitrate complexes, extends its applications in organic synthesis. The present review highlights recent advances of copper nitrate in organic synthesis, along with the mechanisms.

Mining information dependency in outpatient encounters for chronic disease care.

Chronic disease care, e.g., care of type 2 diabetes mellitus, is a long-term, complex process involving collaboration and coordination among multiple healthcare providers. To facilitate and accelerate the process, it is key to understand the information flow and identify the information dependency (e.g., temporal dependency of co-occurrence and sequential occurrence) during care provision, which is also the objective of this work. Since most health interventions and decisions are made in outpatient encounters for chronic patients, in this paper, we propose an approach to mine temporal information dependency in outpatient encounter records using sequential pattern mining techniques. By exploring the real data of over 10,000 type 2 diabetes patients from three hospitals, the proposed approach effectively works out sets of meaningful information dependency patterns for different patient groups. The discovered information dependency can be used to guide the information sharing between different health providers, and optimize the chronic disease care coordination.

A framework for evaluating sequential clinical interventions with disease progression.

This work proposes to leverage an advanced modeling technique, namely Markov Decision Process, to evaluate sequential clinical interventions in disease management. We have demonstrated our evaluation framework on a diabetes case study over two real data sets, and discovered valuable clinical insights towards better interventions during disease progression.

Using data mining techniques on discovering physician practice patterns regarding to medication prescription - an exploratory study.

In this paper, we propose a data mining method for exploring the decision-making processes of physicians from electronic patient records and test it on the medical records of patients with type-2 diabetes mellitus. This method runs in two modes: general and partitioned. In the general mode, it mines rules from the whole medical records. In the partitioned mode, with a given partition factor, medical records are assigned into categories and a corresponding set of rules will be discovered for each category. Medication prescription predictions can be provided based on these rules. By comparing mined rules and prescription prediction accuracy under different modes, we discover that: 1) both the averaged precision and recall rate of the general mode can reach around 80%; 2) physicians tend to conform to the guideline instead of having their own preferences; 3) the medication decision can be affected by some hidden factors. These findings suggest this method show promise in discovering physician practice patterns and obtaining insights from real medical records.

Improving physician practice efficiency by learning lab test ordering pattern.

The system of electronic medical records (EMR) has been widely used in physician practice. In China, physicians have the time pressure to provide care to many patients in a short period. Improving practice efficiency is a promising direction to mitigate this predicament. During the encounter, ordering lab test is one of the most frequent actions in EMR system. In this paper, our motivation is to save physician's time by providing lab test ordering list to facilitate physician practice. To this end, we developed weight based multi-label classification framework to learn to order lab test for the current encounter according to the historical EMR. Particularly, we propose to learn the physician-specific lab test ordering pattern as different physicians may have different practice behavior on the same population. Experimental results on the real data set demonstrate that physician-specific models can outperform the baseline.

An efficacy driven approach for medication recommendation in type 2 diabetes treatment using data mining techniques.

We demonstrate how data mining techniques can help recommend effective medications when physicians need to control the glucose level of patients with type 2 diabetes. We first identify the factors that may affect physicians' medication decisions and then develop a patient-similarity based approach to automatically recommend medications for a patient with the specific condition so that his blood glucose level (measured by HbA1C value) can be well controlled. The approach is validated through experiments on real data sets and compared with the recommendations by following a clinical guideline.

Epitomized summarization of wireless capsule endoscopic videos for efficient visualization.

A video recording of an examination by Wireless Capsule Endoscopy (WCE) may typically contain more than 55,000 video frames, which makes the manual visual screening by an experienced gastroenterologist a highly time-consuming task. In this paper, we propose a novel method of epitomized summarization of WCE videos for efficient visualization to a gastroenterologist. For each short sequence of a WCE video, an epitomized frame is generated. New constraints are introduced into the epitome formulation to achieve the necessary visual quality for manual examination, and an EM algorithm for learning the epitome is derived. First, the local context weights are introduced to generate the epitomized frame. The epitomized frame preserves the appearance of all the input patches from the frames of the short sequence. Furthermore, by introducing spatial distributions for semantic interpretation of image patches in our epitome formulation, we show that it also provides a framework to facilitate the semantic description of visual features to generate organized visual summarization of WCE video, where the patches in different positions correspond to different semantic information. Our experiments on real WCE videos show that, using epitomized summarization, the number of frames have to be examined by the gastroenterologist can be reduced to less than one-tenth of the original frames in the video.

Modeling interaction for segmentation of neighboring structures.

This paper presents a new method for segmenting medical images by modeling interaction between neighboring structures. Compared to previously reported methods, the proposed approach enables simultaneous segmentation of multiple neighboring structures for improved robustness. During the segmentation process, the object contour evolution and shape prior estimates are influenced by the interactions between neighboring shapes consisting of attraction, repulsion, and competition. Instead of estimating the a priori shape of each structure independently, an interactive maximum a posteriori shape estimation method is used for estimating the shape priors by considering shape prior distribution, neighboring shapes, and image features. Energy functionals are then formulated to model the interaction and segmentation. With the proposed method, neighboring structures with similar intensities and/or textures, and blurred boundaries can be extracted simultaneously. Experimental results obtained on both synthetic data and medical images demonstrate that the introduced interaction between neighboring structures improves segmentation performance compared with other existing approaches.

Segmentation of neighboring organs in medical image with model competition.

This paper presents a novel approach for image segmentation by introducing competition between neighboring shape models. Our method is motivated by the observation that evolving neighboring contours should avoid overlapping with each other and this should be able to aid in multiple neighboring objects segmentation. A novel energy functional is proposed, which incorporates both prior shape information and interactions between deformable models. Accordingly, we also propose an extended maximum a posteriori (MAP) shape estimation model to obtain the shape estimate of the organ. The contours evolve under the influence of image information, their own shape priors and neighboring MAP shape estimations using level set methods to recover organ shapes. Promising results and comparisons from experiments on both synthetic data and medical imagery demonstrate the potential of our approach.