Vaporized nicotine in utero results in reduced birthweight, increased locomotion, and decreased voluntary exercise, dependent on sex and diet in offspring.

Rationale Clinical research has shown that prenatal exposure to nicotine may result in increased obesity risk later in life. Preclinical research has corroborated this finding, but few studies have investigated inhaled nicotine or the interaction with diet on obesity risk. Objective The aim of this study was to investigate the effects of prenatal nicotine exposure on both direct and indirect obesity measures, with both sex and diet as factors. Methods Pregnant rats were exposed to either vehicle or nicotine vapor (24 mg/mL or 59 mg/mL) throughout the entire gestational period. Offspring from each treatment group were given either a normal diet or a high fat diet starting at postnatal day 22. Caloric intake, body weight, spontaneous locomotion, sleep/wake activity, and voluntary exercise were measured throughout adolescence. Pregnancy weight gain and pup birthweights were collected to further measure developmental effects of prenatal nicotine exposure. Results Both maternal weight gain during pregnancy and pup weight at birth were decreased with prenatal nicotine exposure. Early adolescent males showed increased spontaneous activity in the open field following prenatal nicotine exposure compared to vehicle counterparts, particularly those given high-fat diet. Additionally, high dose nicotine prenatal treated males ran significantly less distance on the running wheel in late adolescence compared to vehicle counterparts, in the normal diet group only. Conclusion The results presented here show decreased birthweight, hyperactivity, and decreased voluntary exercise in adolescence following prenatal nicotine exposure in dose, sex, and diet dependent manners, which could lead to increased obesity risk in adulthood.

Topical hidden genome: discovering latent cancer mutational topics using a Bayesian multilevel context-learning approach.

Inferring the cancer-type specificities of ultra-rare, genome-wide somatic mutations is an open problem. Traditional statistical methods cannot handle such data due to their ultra-high dimensionality and extreme data sparsity. To harness information in rare mutations, we have recently proposed a formal multilevel multilogistic "hidden genome" model. Through its hierarchical layers, the model condenses information in ultra-rare mutations through meta-features embodying mutation contexts to characterize cancer types. Consistent, scalable point estimation of the model can incorporate 10s of millions of variants across thousands of tumors and permit impressive prediction and attribution. However, principled statistical inference is infeasible due to the volume, correlation, and noninterpretability of mutation contexts. In this paper, we propose a novel framework that leverages topic models from computational linguistics to effectuate dimension reduction of mutation contexts producing interpretable, decorrelated meta-feature topics. We propose an efficient MCMC algorithm for implementation that permits rigorous full Bayesian inference at a scale that is orders of magnitude beyond the capability of existing out-of-the-box inferential high-dimensional multi-class regression methods and software. Applying our model to the Pan Cancer Analysis of Whole Genomes dataset reveals interesting biological insights including somatic mutational topics associated with UV exposure in skin cancer, aging in colorectal cancer, and strong influence of epigenome organization in liver cancer. Under cross-validation, our model demonstrates highly competitive predictive performance against blackbox methods of random forest and deep learning.

Identifying somatic fingerprints of cancers defined by germline and environmental risk factors.

Numerous studies over the past generation have identified germline variants that increase specific cancer risks. Simultaneously, a revolution in sequencing technology has permitted high-throughput annotations of somatic genomes characterizing individual tumors. However, examining the relationship between germline variants and somatic alteration patterns is hugely challenged by the large numbers of variants in a typical tumor, the rarity of most individual variants, and the heterogeneity of tumor somatic fingerprints. In this article, we propose statistical methodology that frames the investigation of germline-somatic relationships in an interpretable manner. The method uses meta-features embodying biological contexts of individual somatic alterations to implicitly group rare mutations. Our team has used this technique previously through a multilevel regression model to diagnose with high accuracy tumor site of origin. Herein, we further leverage topic models from computational linguistics to achieve interpretable lower-dimensional embeddings of the meta-features. We demonstrate how the method can identify distinctive somatic profiles linked to specific germline variants or environmental risk factors. We illustrate the method using The Cancer Genome Atlas whole-exome sequencing data to characterize somatic tumor fingerprints in breast cancer patients with germline BRCA1/2 mutations and in head and neck cancer patients exposed to human papillomavirus.

Comparison of Surgical Techniques for the Treatment of Congenital Nasal Pyriform Aperture Stenosis: A Systematic Review.

INTRODUCTION: Congenital nasal pyriform aperture stenosis (CNPAS) is a rare condition that results in neonatal respiratory difficulty. The purpose of this systematic review was to compare surgical outcomes of drilling versus dilation techniques in the treatment of CNPAS. METHODS: Pubmed, Embase, and Cochrane Clinical Trials databases were searched for terms "congenital nasal pyriform aperture stenosis" or "pyriform aperture stenosis" from 2010 to 2021. Twenty-five studies were included that evaluated pediatric patients treated surgically for CNPAS with available outcomes data including complications, revisions, and length of stay. RESULTS: A total of 51 patients with CNPAS were pooled from included studies. The median age was 29 days, 56.9% were female, and 54.9% were born full-term. The median pyriform aperture width before surgery was 5.00 mm (IQR = 4.10, 6.45). Forty (78.4%) patients underwent sublabial drilling, while 6 had a dilation procedure performed with hegar cervical dilators, 2 had a balloon dilation, and 3 were dilated with either an acrylic device, endotracheal tube, or bougie. There were no post-operative complications for 76.5% of patients, while a second surgery was required in 9 (17.6%) patients. The median length of stay was 11 days (IQR = 4, 26). No statistically significant difference was observed between sublabial drilling and surgical dilation techniques with respect to complications, need for revision surgery, or length of stay. CONCLUSION: Current literature is insufficient to determine if drilling or dilation is more effective in the treatment of CNPAS.

Vaporized Δ9-THC in utero results in reduced birthweight, increased locomotion, and altered wake-cycle activity dependent on dose, sex, and diet in the offspring.

AIMS: Preclinical studies have found that chronic ∆9-tetrahydrocannabinol (THC) treatment is directly associated with weight gain when introduced during adolescence and adulthood, but the effect of prenatal THC is unclear. Clinical studies have demonstrated prenatal exposure to THC is a prospective predictor of increased health risks associated with obesity. Our study aims to examine prenatal THC impact on obesity risks in males and females throughout adolescence using a clinically relevant inhalation model. METHODS: Pregnant rats were exposed to one of the following from gestational day 2 through birth: 10 mg THC, 40 mg THC, or air. Daily 10-min inhalations were conducted in each animal from 0900 to 1200. Offspring from each treatment group were given either a high-fat diet (HFD) or a normal diet (ND). Food and bodyweights were collected daily, while circadian activity, locomotion, and exercise were measured periodically (PND 21-60). Pregnancy weight gain and birth weight were collected to determine early-life developmental effects. RESULTS: Rats prenatally treated with low-dose THC (LDTHC) generally had lower dark-cycle activity compared with control counterparts, but this altered activity was not observed at the higher dose of THC (HDTHC). In terms of open-field activity, THC doses displayed a general increase in locomotion. In addition, the LDTHC male rats in the ND showed significantly greater exploratory behavior. Prenatal THC had dose-dependent effects on maternal weight gain and birth weight. CONCLUSIONS: Overall, our findings indicate there are some activity-related and developmental effects of prenatal THC, which may be related to obesity risks later in life.

Robust Principal Component Analysis: A Median of Means Approach.

Principal component analysis (PCA) is a fundamental tool for data visualization, denoising, and dimensionality reduction. It is widely popular in statistics, machine learning, computer vision, and related fields. However, PCA is well-known to fall prey to outliers and often fails to detect the true underlying low-dimensional structure within the dataset. Following the Median of Means (MoM) philosophy, recent supervised learning methods have shown great success in dealing with outlying observations without much compromise to their large sample theoretical properties. This article proposes a PCA procedure based on the MoM principle. Called the MoMPCA, the proposed method is not only computationally appealing but also achieves optimal convergence rates under minimal assumptions. In particular, we explore the nonasymptotic error bounds of the obtained solution via the aid of the Rademacher complexities while granting absolutely no assumption on the outlying observations. The derived concentration results are not dependent on the dimension because the analysis is conducted in a separable Hilbert space, and the results only depend on the fourth moment of the underlying distribution in the corresponding norm. The proposal's efficacy is also thoroughly showcased through simulations and real data applications.

Dual-Scan Photoacoustic Tomography for the Imaging of Vascular Structure on Foot.

Chronic leg ulcers are affecting approximately 6.5 million Americans, and they are associated with significant mortality, reduced quality of life, and high treatment costs. Since many chronic ulcers have underlying vascular insufficiency, accurate assessment of tissue perfusion is critical to treatment planning and monitoring. This study introduces a dual-scan photoacoustic (PA) tomography (PAT) system that can simultaneously image the dorsal and plantar sides of the foot to reduce imaging time. To account for the unique shape of the foot, the system employs height-adjustable and articulating baseball stages that can scan along the foot's contour. In vivo results from healthy volunteers demonstrate the system's ability to acquire clear images of foot vasculature, and results from patients indicate that the system can image patients with various ulcer conditions. We also investigated various PA features and examined their correlation with the foot condition. Our preliminary results indicate that vessel sharpness, occupancy, intensity, and density could all be used to assess tissue perfusion. This research demonstrated the potential of PAT for routine clinical tissue perfusion assessment.

Implicit Annealing in Kernel Spaces: A Strongly Consistent Clustering Approach.

Kernel k-means clustering is a powerful tool for unsupervised learning of non-linearly separable data. Its merits are thoroughly validated on a suite of simulated datasets and real data benchmarks that feature nonlinear and multi-view separation. Since the earliest attempts, researchers have noted that such algorithms often become trapped by local minima arising from the non-convexity of the underlying objective function. In this paper, we generalize recent results leveraging a general family of means to combat sub-optimal local solutions to the kernel and multi-kernel settings. Called Kernel Power k-Means, our algorithm uses majorization-minimization (MM) to better solve this non-convex problem. We show that the method implicitly performs annealing in kernel feature space while retaining efficient, closed-form updates. We rigorously characterize its convergence properties both from computational and statistical points of view. In particular, we characterize the large sample behavior of the proposed method by establishing strong consistency guarantees as well as finite-sample bounds on the excess risk of the estimates through modern tools in learning theory. The proposal's efficacy is demonstrated through an array of simulated and real data experiments.

On Consistent Entropy-Regularized k-Means Clustering With Feature Weight Learning: Algorithm and Statistical Analyses.

Clusters in real data are often restricted to low-dimensional subspaces rather than the entire feature space. Recent approaches to circumvent this difficulty are often computationally inefficient and lack theoretical justification in terms of their large-sample behavior. This article deals with the problem by introducing an entropy incentive term to efficiently learn the feature importance within the framework of center-based clustering. A scalable block-coordinate descent algorithm, with closed-form updates, is incorporated to minimize the proposed objective function. We establish theoretical guarantees on our method by Vapnik-Chervonenkis (VC) theory to establish strong consistency along with uniform concentration bounds. The merits of our method are showcased through detailed experimental analysis on toy examples as well as real data clustering benchmarks.

Study of the Interface and Radial Dopant Position in Semiconductor Heterostructures Using X-ray Absorption Spectroscopy.

Two questions that remain a challenge in the field of colloidal doped core/shell nanomaterials of different morphologies are the nature of the interface and the radial location of the dopant ion due to the diffusion within the lattice. Using a model system of Cu-doped CdSe/CdS quantum dots, we develop an in-depth understanding of the extended X-ray absorption fine structure (EXAFS) spectra of the dopant and host atoms to address both issues. Our findings suggest that the interface is not sharp, in agreement with the nonstructural studies in the literature. Local structure analysis around the Cu dopant ion confirms that Cu drifts out from the core toward the outer region in the absence of the shell but stays mostly in the core after the formation of a sufficiently thick interfacial barrier (∼2 monolayers). This study highlights the significance of EXAFS spectroscopy in understanding the nature of the interface in nanomaterials.

On the use of the likelihood ratio test methodology in pharmacovigilance.

The safety of medical products due to adverse events (AE) from drugs, therapeutic biologics, and medical devices is a major public health concern worldwide. Likelihood ratio test (LRT) approaches to pharmacovigilance constitute a class of rigorous statistical tools that permit objective identification of AEs of a specific drug and/or a class of drugs cataloged in spontaneous reporting system databases. However, the existing LRT approaches encounter certain theoretical and computational challenges when an underlying Poisson model assumption is violated, including in cases of zero-inflated data. We briefly review existing LRT approaches and propose a novel class of (pseudo-) LRT methods to address these challenges. Our approach uses an alternative parametrization to formulate a unified framework with a common test statistic that can handle both Poisson and zero-inflated Poisson (ZIP) models. The proposed framework is computationally efficient, and it reveals deeper insights into the comparative behaviors of the Poisson and the ZIP models for handling AE data. Our extensive simulation studies document notably superior performances of the proposed methods over existing approaches particularly under zero-inflation, both in terms of statistical (eg, much better control of the nominal level and false discovery rate with substantially enhanced power) and computational ( ∼ $$ \sim $$ 100-500-fold gains in average running times) performance metrics. An application of our method on the statin drug class from the FDA FAERS database reveals interesting insights on potential AEs. An R package, pvLRT, implementing our methods has been released in the public domain.

Longitudinal changes in cardiac function in Duchenne muscular dystrophy population as measured by magnetic resonance imaging.

BACKGROUND: The lack of dystrophin in cardiomyocytes in Duchenne muscular dystrophy (DMD) is associated with progressive decline in cardiac function eventually leading to death by 20-40 years of age. The aim of this prospective study was to determine rate of progressive decline in left ventricular (LV) function in Duchenne muscular dystrophy (DMD) over 5 years. METHODS: Short axis cine and grid tagged images of the LV were acquired in individuals with DMD (n = 59; age = 5.3-18.0 years) yearly, and healthy controls at baseline (n = 16, age = 6.0-18.3 years) on a 3 T MRI scanner. Grid-tagged images were analyzed for composite circumferential strain (ℇcc%) and ℇcc% in six mid LV segments. Cine images were analyzed for left ventricular ejection fraction (LVEF), LV mass (LVM), end-diastolic volume (EDV), end-systolic volume (ESV), LV atrioventricular plane displacement (LVAPD), and circumferential uniformity ratio estimate (CURE). LVM, EDV, and ESV were normalized to body surface area for a normalized index of LVM (LVMI), EDV (EDVI) and ESV (ESVI). RESULTS: At baseline, LV ℇcc% was significantly worse in DMD compared to controls and five of the six mid LV segments demonstrated abnormal strain in DMD. Longitudinal measurements revealed that ℇcc% consistently declined in individuals with DMD with the inferior segments being more affected. LVEF progressively declined between 3 to 5 years post baseline visit. In a multivariate analysis, the use of cardioprotective drugs trended towards positively impacting cardiac measures while loss of ambulation and baseline age were associated with negative impact. Eight out of 17 cardiac parameters reached a minimal clinically important difference with a threshold of 1/3 standard deviation. CONCLUSION: The study shows a worsening of circumferential strain in dystrophic myocardium. The findings emphasize the significance of early and longitudinal assessment of cardiac function in DMD and identify early biomarkers of cardiac dysfunction to help design clinical trials to mitigate cardiac pathology. This study provides valuable non-invasive and non-contrast based natural history data of cardiac changes which can be used to design clinical trials or interpret the results of current trials aimed at mitigating the effects of decreased cardiac function in DMD.

Photoluminescence Quenching in CsPbCl3 upon Fe Doping: Colloidal Synthesis, Structural and Optical Properties.

Doped perovskite lead halide nanocrystals (PHNCs) are promising materials for various optoelectronic applications, but the major challenge faced by the researchers is the inability to dope foreign elements into perovskite lattice because of the strong lead-halide bond energies. In this work, we have used Fe as a dopant in CsPbCl3 to explore different doping techniques based on the colloidal synthesis of PHNCs to investigate the advantages and disadvantages of different techniques. We are able to dope a relatively higher amount of Fe (∼10%) than reported and observe clear optical signatures when the precursor does not have pre-existing Pb-Cl bonds. We prove that there are two competing processes inside a doped PHNC - one is the effect of dopant energy levels, and the other is surface passivation by halide ions. Using the most optimal synthesis strategy, we show that although Fe does act as a luminescence quencher in perovskite similar to II-VI quantum dots (QDs), the quenching requires much more Fe compared to trace amounts of Fe required in traditional QDs. Our work will assist in giving an overall comparative idea of doping and finding the most optimized strategy and help identify the underlying physical processes in perovskite based QDs.

An epigenetic small molecule screen to target abnormal nuclear morphology in human cells.

Irregular nuclear shapes are a hallmark of human cancers. Recent studies suggest that alterations to chromatin regulators may cause irregular nuclear morphologies. Here we screened an epigenetic small molecule library consisting of 145 compounds against chromatin regulators for their ability to revert abnormal nuclear shapes that were induced by gene knockdown in noncancerous MCF10A human mammary breast epithelial cells. We leveraged a previously validated quantitative Fourier approach to quantify the elliptical Fourier coefficient (EFC ratio) as a measure of nuclear irregularities, which allowed us to perform rigorous statistical analyses of screening data. Top hit compounds fell into three major mode of action categories, targeting three separate epigenetic modulation routes: 1) histone deacetylase inhibitors, 2) bromodomain and extraterminal domain protein inhibitors, and 3) methyl-transferase inhibitors. Some of the top hit compounds were also efficacious in reverting nuclear irregularities in MDA-MB-231 triple negative breast cancer cells and in PANC-1 pancreatic cancer cells in a cell-type-dependent manner. Regularization of nuclear shapes was compound-specific, cell-type specific, and dependent on the specific molecular perturbation that induced nuclear irregularities. Our approach of targeting nuclear abnormalities may be potentially useful in screening new types of cancer therapies targeted toward chromatin structure.

Stable room temperature ferroelectricity in hydrogen-bonded supramolecular assemblies of ambipolar π-systems.

This article reports H-bonding driven supramolecular polymerization of naphthalimide (A)-thiophene (D)-naphthalimide (A) (AD n A, n = 1-4) conjugated ambipolar π-systems and its remarkable impact on room temperature ferroelectricity. Electrochemical studies confirm the ambipolar nature of these AD n A molecules with the HOMO-LUMO gap varying between 2.05 and 2.29 eV. Electron density mapping from ESP calculations reveals intra-molecular charge separation as typically observed in ambipolar systems. In the aggregated state, AD1A and AD2A exhibit bathochromically shifted absorption bands while AD3A and AD4A show typical H-aggregation with a hypsochromic shift. Polarization vs. electric field (P-E) measurements reveal stable room temperature ferroelectricity for these supramolecular assemblies, most prominent for the AD2A system, with a Curie temperature (T c) ≈ 361 K and saturation polarization (P s) of ∼2 µC cm-2 at a rather low coercive field of ∼2 kV cm-1. Control molecules, lacking either the ambipolar chromophore or the amide functionality, do not show any ferroelectricity, vindicating the present molecular and supramolecular design. Computational studies enable structural optimization of the stacked oligomer(s) of AD2A molecules and reveal a significant increase in the macro-dipole moment (in the range of 10-12 Debye) going from the monomer to the oligomer(s), which provides the rationale for the origin of ferroelectricity in these supramolecular polymers.

Detecting Meaningful Clusters From High-Dimensional Data: A Strongly Consistent Sparse Center-Based Clustering Approach.

In context to high-dimensional clustering, the concept of feature weighting has gained considerable importance over the years to capture the relative degrees of importance of different features in revealing the cluster structure of the dataset. However, the popular techniques in this area either fail to perform feature selection or do not preserve the simplicity of Lloyd's heuristic to solve the k-means problem and the like. In this paper, we propose a Lasso Weighted k-means ( LW- k-means) algorithm, as a simple yet efficient sparse clustering procedure for high-dimensional data where the number of features ( p) can be much higher than the number of observations ( n). The LW- k-means method imposes an l1 regularization term involving the feature weights directly to induce feature selection in a sparse clustering framework. We develop a simple block-coordinate descent type algorithm with time-complexity resembling that of Lloyd's method, to optimize the proposed objective. In addition, we establish the strong consistency of the LW- k-means procedure. Such an analysis of the large sample properties is not available for the conventional sparse k-means algorithms, in general. LW- k-means is tested on a number of synthetic and real-life datasets and through a detailed experimental analysis, we find that the performance of the method is highly competitive against the baselines as well as the state-of-the-art procedures for center-based high-dimensional clustering, not only in terms of clustering accuracy but also with respect to computational time.

Bayesian analysis of coupled cellular and nuclear trajectories for cell migration.

Cell migration, the process by which cells move from one location to another, plays crucial roles in many biological events. While much research has been devoted to understand the process, most statistical cell migration models rely on using time-lapse microscopy data from cell trajectories alone. However, the cell and its associated nucleus work together to orchestrate cell movement, which motivates a joint analysis of coupled cell-nucleus trajectories. In this paper, we propose a Bayesian hierarchical model for analyzing cell migration. We incorporate a bivariate angular distribution to handle the coupled cell-nucleus trajectories and introduce latent motility status indicators to model a cell's motility as a time-dependent characteristic. A Markov chain Monte Carlo algorithm is provided for practical implementation of our model, which is used on real experimental data from MDA-MB-231 and NIH 3T3 cells. Through the fitted models, deeper insights into the migratory patterns of these experimental cell populations are gained and their differences are quantified.

Prospective assessment of catheter-associated bacteriuria clinical presentation, epidemiology, and colonization dynamics in nursing home residents.

BACKGROUNDCatheterization facilitates continuous bacteriuria, for which the clinical significance remains unclear. This study aimed to determine the clinical presentation, epidemiology, and dynamics of bacteriuria in a cohort of long-term catheterized nursing home residents.METHODSProspective urine culture, urinalysis, chart review, and assessment of signs and symptoms of infection were performed weekly for 19 study participants over 7 months. All bacteria ≥ 1 × 103 cfu/mL were cultured, isolated, identified, and tested for susceptibility to select antimicrobials.RESULTSIn total, 226 of the 234 urine samples were polymicrobial (97%), with an average of 4.7 isolates per weekly specimen. A total of 228 urine samples (97%) exhibited ≥ 1 × 106 CFU/mL, 220 (94%) exhibited abnormal urinalysis, 126 (54%) were associated with at least 1 possible sign or symptom of infection, and 82 (35%) would potentially meet a standardized definition of catheter-associated urinary tract infection (CAUTI), but only 3 had a caregiver diagnosis of CAUTI. Bacterial isolates (286; 30%) were resistant to a tested antimicrobial agent, and bacteriuria composition was remarkably stable despite a combined total of 54 catheter changes and 23 weeks of antimicrobial use.CONCLUSIONBacteriuria composition was largely polymicrobial, including persistent colonization by organisms previously considered to be urine culture contaminants. Neither antimicrobial use nor catheter changes sterilized the urine, at most resulting in transient reductions in bacterial burden followed by new acquisition of resistant isolates. Thus, this patient population exhibits a high prevalence of bacteriuria coupled with potential indicators of infection, necessitating further exploration to identify sensitive markers of true infection.FUNDINGThis work was supported by the NIH (R00 DK105205, R01 DK123158, UL1 TR001412).

Genome-Derived Classification Signature for Ampullary Adenocarcinoma to Improve Clinical Cancer Care.

PURPOSE: The clinical behavior of ampullary adenocarcinoma varies widely. Targeted tumor sequencing may better define biologically distinct subtypes to improve diagnosis and management. EXPERIMENTAL DESIGN: The hidden-genome algorithm, a multilevel meta-feature regression model, was trained on a prospectively sequenced cohort of 3,411 patients (1,001 pancreatic adenocarcinoma, 165 distal bile-duct adenocarcinoma, 2,245 colorectal adenocarcinoma) and subsequently applied to targeted panel DNA-sequencing data from ampullary adenocarcinomas. Genomic classification (i.e., colorectal vs. pancreatic) was correlated with standard histologic classification [i.e., intestinal (INT) vs. pancreatobiliary (PB)] and clinical outcome. RESULTS: Colorectal genomic subtype prediction was primarily influenced by mutations in APC and PIK3CA, tumor mutational burden, and DNA mismatch repair (MMR)-deficiency signature. Pancreatic genomic-subtype prediction was dictated by KRAS gene alterations, particularly KRAS G12D, KRAS G12R, and KRAS G12V. Distal bile-duct adenocarcinoma genomic subtype was most influenced by copy-number gains in the MDM2 gene. Despite high (73%) concordance between immunomorphologic subtype and genomic category, there was significant genomic heterogeneity within both histologic subtypes. Genomic scores with higher colorectal probability were associated with greater survival compared with those with a higher pancreatic probability. CONCLUSIONS: The genomic classifier provides insight into the heterogeneity of ampullary adenocarcinoma and improves stratification, which is dictated by the proportion of colorectal and pancreatic genomic alterations. This approach is reproducible with available molecular testing and obviates subjective histologic interpretation.

Molecular Recognition Driven Bioinspired Directional Supramolecular Assembly of Amphiphilic (Macro)molecules and Proteins.

Bioinspired self-assembly has been explored with diverse synthetic scaffolds, among which amphiphiles are perhaps the most extensively studied systems. Classical surfactants or amphiphilic block copolymers, depending on the hydrophobic-hydrophilic balance, produce distinct nanostructures, which hold promise for applications ranging from biology to materials sciences. Nevertheless, their immiscibility-driven aggregation does not provide the opportunity to precisely regulate the internal order, morphology, or functional group display, which is highly desirable, especially in the context of biological applications.A new class of amphiphiles have emerged in the recent past in which the hydrophilic segment(s) is appended with a hydrophobic supramolecular-structure-directing-unit (SSDU), consisting of a π-conjugated chromophore and a H-bonding group. Self-recognition of the SSDU by attractive directional interactions governs the supramolecular assembly, which is fundamentally different than the repulsive solvent-immiscibility driven aggregation of traditional amphiphiles. Such SSDU-appended hydrophilic polymers exhibit entropy-driven highly stable self-assembly producing distinct nanostructures depending on the H-bonding functional group. For example, polymers with the hydrazide-functionalized SSDU attached form a polymersome, while in a sharp contrast, the same polymers when connected to an amide containing SSDU produce a cylindrical micelle via a spherical-micelle intermediate. This relationship holds true for a series of SSDU-attached hydrophilic polymers irrespective of the hydrophobic/hydrophilic balance or chemical structure, indicating that the supramolecular-assembly is primarily controlled by the specific molecular-recognition motif of the SSDU, instead of the packing parameter-based norms. Beyond synthetic polymers, SSDU-attached proteins also exhibit similar molecular-recognition driven self-assembly as well as coassembly with SSDU-attached polymers or hydrophilic wedges, producing multi-stimuli-responsive nanostructures in which the protein gains remarkable protection from thermal denaturation or enzymatic hydrolysis and exhibits redox-responsive enzymatic activity.Furthermore, SSDU-derived bola-shape π-amphiphiles have been recognized as a useful scaffold for the synthesis of unsymmetric polymersomes, rarely reported in the literature. The building block consists of a hydrophobic naphthalene-diimide (NDI) π-system attached to a hydrophilic functional group (ionic or nonionic) and a nonionic wedge on its two opposite arms. Extended H-bonding among the hydrazide groups, placed only on one side of the central chromophore by design, ensures stacking of the NDIs with parallel orientation and induces a preferred direction of curvature so that the H-bonded chain and consequently the functional groups attached to the same side remain at the inner-wall of the supramolecular polymersome. Automatically, the functional groups, located on the other side, are displayed at the outer surface. This design works for different amphiphiles, which by virtue of efficient and predictable functional group display, strongly influences the multivalent binding with different biological targets resulting in efficient enzyme inhibition, glycocluster effect, or antibacterial activity, depending on the nature of the functional group. By taking advantage of the electron accepting nature of the NDI, electron rich pyrene-containing amphiphiles can be costacked in alternating sequence, producing temperature and redox-responsive supramolecular polymers with NDI/pyrene stoichiometry-dependent morphology, lower critical solution temperature (LCST), functional group display, and antibacterial activity.