Personalized treatment selection via product partition models with covariates.

Precision medicine is an approach for disease treatment that defines treatment strategies based on the individual characteristics of the patients. Motivated by an open problem in cancer genomics, we develop a novel model that flexibly clusters patients with similar predictive characteristics and similar treatment responses; this approach identifies, via predictive inference, which one among a set of treatments is better suited for a new patient. The proposed method is fully model based, avoiding uncertainty underestimation attained when treatment assignment is performed by adopting heuristic clustering procedures, and belongs to the class of product partition models with covariates, here extended to include the cohesion induced by the normalized generalized gamma process. The method performs particularly well in scenarios characterized by considerable heterogeneity of the predictive covariates in simulation studies. A cancer genomics case study illustrates the potential benefits in terms of treatment response yielded by the proposed approach. Finally, being model based, the approach allows estimating clusters' specific response probabilities and then identifying patients more likely to benefit from personalized treatment.

Amyotrophic lateral sclerosis stratification: unveiling patterns with virome, inflammation, and metabolism molecules.

Amyotrophic lateral sclerosis (ALS) is an untreatable and clinically heterogeneous condition primarily affecting motor neurons. The ongoing quest for reliable biomarkers that mirror the disease status and progression has led to investigations that extend beyond motor neurons' pathology, encompassing broader systemic factors such as metabolism, immunity, and the microbiome. Our study contributes to this effort by examining the potential role of microbiome-related components, including viral elements, such as torque tenovirus (TTV), and various inflammatory factors, in ALS. In our analysis of serum samples from 100 ALS patients and 34 healthy controls (HC), we evaluated 14 cytokines, TTV DNA load, and 18 free fatty acids (FFA). We found that the evaluated variables are effective in differentiating ALS patients from healthy controls. In addition, our research identifies four unique patient clusters, each characterized by distinct biological profiles. Intriguingly, no correlations were found with site of onset, sex, progression rate, phenotype, or C9ORF72 expansion. A remarkable aspect of our findings is the discovery of a gender-specific relationship between levels of 2-ethylhexanoic acid and patient survival. In addition to contributing to the growing body of evidence suggesting altered peripheral immune responses in ALS, our exploratory research underscores metabolic diversity challenging conventional clinical classifications. If our exploratory findings are validated by further research, they could significantly impact disease understanding and patient care customization. Identifying groups based on biological profiles might aid in clustering patients with varying responses to treatments.

Protected Poly(3-sulfopropyl methacrylate) Copolymers: Synthesis, Stability, and Orthogonal Deprotection.

Because of their permanent charge, strong polyelectrolytes remain challenging to characterize, in particular, when they are combined with hydrophobic features. For this reason, they are typically prepared through a postmodification of a fully hydrophobic precursor. Unfortunately, these routes often result in an incomplete functionalization or otherwise require harsh reaction conditions, thus limiting their applicability. To overcome these problems, in this work a strategy is presented that facilitates the preparation of well-defined strong polyanions by starting from protected 3-sulfopropyl methacrylate monomers. Depending on the chemistry of the protecting group, the hydrophobic precursor could be quantitatively converted into a strong polyanion under nucleophilic, acidic, or basic conditions. As a proof of concept, orthogonally protected diblock copolymers were synthesized, selectively deprotected, and allowed to self-assemble in aqueous solution. Further conversion into a fully water-soluble polyanion was achieved by deprotecting the second block as well.

Free Fatty Acids Signature in Human Intestinal Disorders: Significant Association between Butyric Acid and Celiac Disease.

Altered circulating levels of free fatty acids (FFAs), namely short chain fatty acids (SCFAs), medium chain fatty acids (MCFAs), and long chain fatty acids (LCFAs), are associated with metabolic, gastrointestinal, and malignant diseases. Hence, we compared the serum FFA profile of patients with celiac disease (CD), adenomatous polyposis (AP), and colorectal cancer (CRC) to healthy controls (HC). We enrolled 44 patients (19 CRC, 9 AP, 16 CD) and 16 HC. We performed a quantitative FFA evaluation with the gas chromatography-mass spectrometry method (GC-MS), and we performed Dirichlet-multinomial regression in order to highlight disease-specific FFA signature. HC showed a different composition of FFAs than CRC, AP, and CD patients. Furthermore, the partial least squares discriminant analysis (PLS-DA) confirmed perfect overlap between the CRC and AP patients and separation of HC from the diseased groups. The Dirichlet-multinomial regression identified only strong positive association between CD and butyric acid. Moreover, CD patients showed significant interactions with age, BMI, and gender. In addition, among patients with the same age and BMI, being male compared to being female implies a decrease of the CD effect on the (log) prevalence of butyric acid in FFA composition. Our data support GC-MS as a suitable method for the concurrent analysis of circulating SCFAs, MCFAs, and LCFAs in different gastrointestinal diseases. Furthermore, and notably, we suggest for the first time that butyric acid could represent a potential biomarker for CD screening.

Significant and Conflicting Correlation of IL-9 With Prevotella and Bacteroides in Human Colorectal Cancer.

Background and aim: Gut microbiota (GM) can support colorectal cancer (CRC) progression by modulating immune responses through the production of both immunostimulatory and/or immunosuppressive cytokines. The role of IL-9 is paradigmatic because it can either promote tumor progression in hematological malignancies or inhibit tumorigenesis in solid cancers. Therefore, we investigate the microbiota-immunity axis in healthy and tumor mucosa, focusing on the correlation between cytokine profile and GM signature. Methods: In this observational study, we collected tumor (CRC) and healthy (CRC-S) mucosa samples from 45 CRC patients, who were undergoing surgery in 2018 at the Careggi University Hospital (Florence, Italy). First, we characterized the tissue infiltrating lymphocyte subset profile and the GM composition. Subsequently, we evaluated the CRC and CRC-S molecular inflammatory response and correlated this profile with GM composition, using Dirichlet multinomial regression. Results: CRC samples displayed higher percentages of Th17, Th2, and Tregs. Moreover, CRC tissues showed significantly higher levels of MIP-1α, IL-1α, IL-1ß, IL-2, IP-10, IL-6, IL-8, IL-17A, IFN-γ, TNF-α, MCP-1, P-selectin, and IL-9. Compared to CRC-S, CRC samples also showed significantly higher levels of the following genera: Fusobacteria, Proteobacteria, Fusobacterium, Ruminococcus2, and Ruminococcus. Finally, the abundance of Prevotella spp. in CRC samples negatively correlated with IL-17A and positively with IL-9. On the contrary, Bacteroides spp. presence negatively correlated with IL-9. Conclusions: Our data consolidate antitumor immunity impairment and the presence of a distinct microbiota profile in the tumor microenvironment compared with the healthy mucosa counterpart. Relating the CRC cytokine profile with GM composition, we confirm the presence of bidirectional crosstalk between the immune response and the host's commensal microorganisms. Indeed, we document, for the first time, that Prevotella spp. and Bacteroides spp. are, respectively, positively and negatively correlated with IL-9, whose role in CRC development is still under debate.

Recognition of Images Degraded by Gaussian Blur.

In this paper, we propose a new theory of invariants to Gaussian blur. We introduce a notion of a primordial image as a canonical form of all Gaussian blur-equivalent images. The primordial image is defined in spectral domain by means of projection operators. We prove that the moments of the primordial image are invariant to Gaussian blur and we derive recursive formulas for their direct computation without actually constructing the primordial image itself. We show how to extend their invariance also to image rotation. The application of these invariants is in blur-invariant image comparison and recognition. In the experimental part, we perform an exhaustive comparison with two main competitors: 1) the Zhang distance and 2) the local phase quantization.

Registration of images with N -fold dihedral blur.

In this paper, we extend our recent registration method designed specifically for registering blurred images. The original method works for unknown blurs, assuming the blurring point-spread function (PSF) exhibits an N -fold rotational symmetry. Here, we also generalize the theory to the case of dihedrally symmetric blurs, which are produced by the PSFs having both rotational and axial symmetries. Such kind of blurs are often found in unfocused images acquired by digital cameras, as in out-of-focus shots the PSF typically mimics the shape of the shutter aperture. This makes our registration algorithm particularly well-suited in applications where blurred image registration must be used as a preprocess step of an image fusion algorithm, and where common registration methods fail, due to the amount of blur. We demonstrate that the proposed method leads to an improvement of the registration performance, and we show its applicability to real images by providing successful examples of blurred image registration followed by depth-of-field extension and multichannel blind deconvolution.

Blur invariant translational image registration for N-fold symmetric blurs.

In this paper, we propose a new registration method designed particularly for registering differently blurred images. Such a task cannot be successfully resolved by traditional approaches. Our method is inspired by traditional phase correlation, which is now applied to certain blur-invariant descriptors instead of the original images. This method works for unknown blurs assuming the blurring PSF exhibits an N-fold rotational symmetry. It does not require any landmarks. We have experimentally proven its good performance, which is not dependent on the amount of blur. In this paper, we explicitly address only registration with respect to translation, but the method can be readily generalized to rotation and scaling.