Application of rational enzyme engineering in a new route to etonogestrel and levonorgestrel: carbonyl reductase bioreduction of ethyl secodione.

Women in developing countries still face enormous challenges when accessing reproductive health care. Access to voluntary family planning empowers women allowing them to complete their education and join the paid workforce. This effectively helps to end poverty, hunger and promotes good health for all. According to the United Nations (UN) organization, in 2022, an estimated 257 million women still lacked access to safe and effective family planning methods globally. One of the main barriers is the associated cost of modern contraceptive methods. Funded by the Bill & Melinda Gates Foundation, Almac Group worked on the development of a novel biocatalytic route to etonogestrel and levonorgestrel, two modern contraceptive APIs, with the goal of substantially decreasing the cost of production and so enabling their use in developing nations. This present work combines the selection and engineering of a carbonyl reductase (CRED) enzyme from Almac's selectAZyme™ panel, with process development, to enable efficient and economically viable bioreduction of ethyl secodione to (13R,17S)-secol, the key chirality introducing intermediate en route to etonogestrel and levonorgestrel API. CRED library screening returned a good hit with an Almac CRED from Bacillus weidmannii, which allowed for highly stereoselective bioreduction at low enzyme loading of less than 1% w/w under screening assay conditions. However, the only co-solvent tolerated was DMSO up to ∼30% v/v, and it was impossible to achieve reaction completion with any enzyme loading at substrate titres of 20 g L-1 and above, due to the insolubility of the secodione. This triggered a rapid enzyme engineering program fully based on computational mutant selection. A small panel of 93 CRED mutants was rationally designed to increase the catalytic activity as well as thermal and solvent stability. The best mutant, Mutant-75, enabled a reaction at 45 °C to go to completion at 90 g L-1 substrate titre in a buffer/DMSO/heptane reaction medium fed over 6 h with substrate DMSO stock solution, with a low enzyme loading of 3.5% w/w wrt substrate. In screening assay conditions, Mutant-75 also showed a 2.2-fold activity increase. Our paper shows which computations and rational decisions enabled this outcome.

Chemical reversion of age-related oocyte dysfunction fails to enhance embryo development in a bovine model of postovulatory aging.

PURPOSE: There are no clinical treatments to prevent/revert age-related alterations associated with oocyte competence decline in the context of advanced maternal age. Those alterations have been attributed to oxidative stress and mitochondrial dysfunction. Our study aimed to test the hypothesis that in vitro maturation (IVM) medium supplementation with antioxidants (resveratrol or phloretin) may revert age-related oocyte competence decline. METHODS: Bovine immature oocytes were matured in vitro for 23 h (young) and 30 h (aged). Postovulatory aged oocytes (control group) and embryos obtained after fertilization were examined and compared with oocytes supplemented with either 2 µM of resveratrol or 6 µM phloretin (treatment groups) during IVM. RESULTS: Aged oocytes had a significantly lower mitochondrial mass and proportion of mitochondrial clustered pattern, lower ooplasmic volume, higher ROS, lower sirtuin-1 protein level, and a lower blastocyst rate in comparison to young oocytes, indicating that postovulatory oocytes have a lower quality and developmental competence, thus validating our experimental model. Supplementation of IVM medium with antioxidants prevented the generation of ROS and restored the active mitochondrial mass and pattern characteristic of younger oocytes. Moreover, sirtuin-1 protein levels were also restored but only following incubation with resveratrol. Despite these findings, the blastocyst rate of treatment groups was not significantly different from the control group, indicating that resveratrol and phloretin could not restore the oocyte competence of postovulatory aged oocytes. CONCLUSION: Resveratrol and phloretin can both revert the age-related oxidative stress and mitochondrial dysfunction during postovulatory aging but were insufficient to enhance embryo developmental rates under our experimental conditions.

Near-Infrared Light Activated Formulation for the Spatially Controlled Release of CRISPR-Cas9 Ribonucleoprotein for Brain Gene Editing.

The CRISPR/Cas9 system has emerged as a promising platform for gene editing; however, the lack of an efficient and safe delivery system to introduce it into cells continues to hinder clinical translation. Here, we report a rationally designed gene-editing nanoparticle (NP) formulation for brain applications: an sgRNA:Cas9 ribonucleoprotein complex is immobilized on the NP surface by oligonucleotides that are complementary to the sgRNA. Irradiation of the formulation with a near-infrared (NIR) laser generates heat in the NP, leading to the release of the ribonucleoprotein complex. The gene-editing potential of the formulation was demonstrated in vitro at the single-cell level. The safety and gene editing of the formulation were also demonstrated in the brains of reporter mice, specifically in the subventricular zone after intracerebral administration and in the olfactory bulb after intranasal administration. The formulation presented here offers a new strategy for the spatially controlled delivery of the CRISPR system to the brain.

Designing Cell Delivery Peptides and SARS-CoV-2-Targeting Small Interfering RNAs: A Comprehensive Bioinformatics Study with Generative Adversarial Network-Based Peptide Design and In Vitro Assays.

Nucleic acid technologies with designed intracellular delivery systems are some of the most promising therapies of the future. Small interfering (si)RNAs inhibit gene expression and protein synthesis and may complement current vaccines with faster design and production. Although successful delivery remains an issue, delivery peptides may help to fill this gap. Here, we address this issue by applying bioinformatic approaches to design new putative cell delivery peptides and siRNAs for COVID-19 variants and other related viral diseases. Of the 29,880 RNA sequences analyzed, 62 were identified in silico as able to target the virus mRNA sequence, and from the 9,984 peptide sequences analyzed, 10 were selected as delivery peptides. From the latter, we further performed in vitro studies of the two best-ranked peptides and compared them with the broadly used TAT delivery peptide. One of them, seq5, displayed better internalization results with about double intensity signal compared to TAT after a 1 h incubation time in GFP-HeLa cells. This peptide has, thus, the features of a delivery peptide and could be used for cargo intracellular delivery.

Using Markov chains and temporal alignment to identify clinical patterns in Dementia.

In the healthcare sector, resorting to big data and advanced analytics is a great advantage when dealing with complex groups of patients in terms of comorbidities, representing a significant step towards personalized targeting. In this work, we focus on understanding key features and clinical pathways of patients with multimorbidity suffering from Dementia. This disease can result from many heterogeneous factors, potentially becoming more prevalent as the population ages. We present a set of methods that allow us to identify medical appointment patterns within a cohort of 1924 patients followed from January 2007 to August 2021 in Hospital da Luz (Lisbon), and to stratify patients into subgroups that exhibit similar patterns of interaction. With Markov Chains, we are able to identify the most prevailing medical appointments attended by Dementia patients, as well as recurring transitions between these. To perform patient stratification, we applied AliClu, a temporal sequence alignment algorithm for clustering longitudinal clinical data, which allowed us to successfully identify patient subgroups with similar medical appointment activity. A feature analysis per cluster obtained allows the identification of distinct patterns and characteristics. This pipeline provides a tool to identify prevailing clinical pathways of medical appointments within the dataset, as well as the most common transitions between medical specialities within Dementia patients. This methodology, alongside demographic and clinical data, has the potential to provide early signalling of the most likely clinical pathways and serve as a support tool for health providers in deciding the best course of treatment, considering a patient as a whole.

Zgli: A Pipeline for Clustering by Compression with Application to Patient Stratification in Spondyloarthritis.

The normalized compression distance (NCD) is a similarity measure between a pair of finite objects based on compression. Clustering methods usually use distances (e.g., Euclidean distance, Manhattan distance) to measure the similarity between objects. The NCD is yet another distance with particular characteristics that can be used to build the starting distance matrix for methods such as hierarchical clustering or K-medoids. In this work, we propose Zgli, a novel Python module that enables the user to compute the NCD between files inside a given folder. Inspired by the CompLearn Linux command line tool, this module iterates on it by providing new text file compressors, a new compression-by-column option for tabular data, such as CSV files, and an encoder for small files made up of categorical data. Our results demonstrate that compression by column can yield better results than previous methods in the literature when clustering tabular data. Additionally, the categorical encoder shows that it can augment categorical data, allowing the use of the NCD for new data types. One of the advantages is that using this new feature does not require knowledge or context of the data. Furthermore, the fact that the new proposed module is written in Python, one of the most popular programming languages for machine learning, potentiates its use by developers to tackle problems with a new approach based on compression. This pipeline was tested in clinical data and proved a promising computational strategy by providing patient stratification via clusters aiding in precision medicine.

Degradation Chemistry and Kinetic Stabilization of Magnetic CrI3.

The discovery of the intrinsic magnetic order in single-layer chromium trihalides (CrX3, X = I, Br, and Cl) has drawn intensive interest due to their potential application in spintronic devices. However, the notorious environmental instability of this class of materials under ambient conditions renders their device fabrication and practical application extremely challenging. Here, we performed a systematic investigation of the degradation chemistry of chromium iodide (CrI3), the most studied among CrX3 families, via a joint spectroscopic and microscopic analysis of the structural and composition evolution of bulk and exfoliated nanoflakes in different environments. Unlike other air-sensitive 2D materials, CrI3 undergoes a pseudo-first-order hydrolysis in the presence of pure water toward the formation of amorphous Cr(OH)3 and hydrogen iodide (HI) with a rate constant of kI = 0.63 day-1 without light. In contrast, a faster pseudo-first-order surface oxidation of CrI3 occurs in a pure O2 environment, generating CrO3 and I2 with a large rate constant of kCr = 4.2 day-1. Both hydrolysis and surface oxidation of CrI3 can be accelerated via light irradiation, resulting in its ultrafast degradation in air. The new chemical insights obtained allow for the design of an effective stabilization strategy for CrI3 with preserved optical and magnetic properties. The use of organic acid solvents (e.g., formic acid) as reversible capping agents ensures that CrI3 nanoflakes remain stable beyond 1 month due to the effective suppression of both hydrolysis and oxidation of CrI3.

Dynamic Bayesian networks for stratification of disease progression in amyotrophic lateral sclerosis.

BACKGROUND AND PURPOSE: Progression rate is quite variable in amyotrophic lateral sclerosis (ALS); thus, tools for profiling disease progression are essential for timely interventions. The objective was to apply dynamic Bayesian networks (DBNs) to establish the influence of clinical and demographic variables on disease progression rate. METHODS: In all, 664 ALS patients from our database were included stratified into slow (SP), average (AP) and fast (FP) progressors, according to the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised (ALSFRS-R) rate of decay. The sdtDBN framework was used, a machine learning model which learnt optimal DBNs with both static (gender, age at onset, onset region, body mass index, disease duration at entry, familial history, revised El Escorial criteria and C9orf72) and dynamic (ALSFRS-R scores and sub-scores, forced vital capacity, maximum inspiratory pressure, maximum expiratory pressure and phrenic amplitude) variables. RESULTS: Disease duration and body mass index at diagnosis are the foremost influences amongst static variables. Disease duration is the variable that better discriminates the three groups. Maximum expiratory pressure is the respiratory test with prevalent influence on all groups. ALSFRS score has a higher influence on FP, but lower on AP and SP. The bulbar sub-score has considerable influence on FP but limited on SP. Limb function has a more decisive influence on AP and SP. The respiratory sub-score has little influence in all groups. ALSFRS-R questions 1 (speech) and 9 (climbing stairs) are the most influential in FP and SP, respectively. CONCLUSIONS: The sdtDBN analysis identified five variables, easily obtained during clinical evaluation, which are the most influential for each progression group. This insightful information may help to improve prognosis and care.

An injectable, naproxen-conjugated, supramolecular hydrogel with ultra-low critical gelation concentration-prepared from a known folate receptor ligand.

Short peptides capped on the N-terminus with aromatic groups are often able to form supramolecular hydrogels-self-assembled networks of fibrils able to trap water molecules. Typically, these hydrogelators can form stiff gels at concentrations of 0.1 to 1.0 wt%-i.e. they consist of mainly water. The properties of these soft materials mimic those of the extracellular matrix (ECM) of biological tissue and therefore they have found many biomedical uses in tissue engineering, wound healing, drug delivery, biosensing and bioprinting applications. In drug delivery strategies related to cancer therapy, injectable hydrogels can serve as a depot formulation, where a sustained release of the chemotherapeutic from near the tumour site allows reduced doses and, therefore, decreased side effects. To further target cancer cells, folic acid-conjugated hydrogels and nanostructures are often sought, to exploit the overexpression of folate receptors on cancer cells-an approach which can allow the selective cellular uptake of an encapsulated drug. In this present study, two known dipeptide folate receptor ligands (1 and 2) recently identified from a screen of a DNA-encoded compound library, were synthesised and investigated for their hydrogelation ability and cytotoxicity. Compound 1, containing a naproxen capping group, rapidly forms hydrogels at concentrations as low as 0.03 wt%-one of the lowest critical gelation concentrations (CGCs) known for a supramolecular hydrogelator. In contrast, compound 2, which contains a 3-indolepropionic acid capping group, was unable to form hydrogels under a range of conditions and concentrations, instead forming nanospheres with diameters of 0.5 µm. Hydrogels of 1 were characterised by STEM microscopy, rheology, fluorescence spectroscopy and circular dichroism. Both compounds 1 and 2 had no impact on the proliferation of kerotinocytes (HaCaT cells) at concentrations up to 100 µM. Compound 1, containing the NSAID, was tested for anti-inflammatory activity in a human cyclooxygenase-1/2 model. The rate of the release of model drug compounds from within hydrogels of 1 was also investigated.

Multispectral Optical Remote Sensing for Water-Leak Detection.

Water losses from water distribution means have a high environmental impact in terms of natural resource depletion (water, energy, ecosystems). This work aims to develop an optical airborne surveillance service for the detection of water leaks (WADI-Water-tightness Airborne Detection Implementation) to provide water utilities with adequate and timely information on leaks in water transportation mains outside urban areas. Firstly, a series of measurement campaigns were performed with two hyperspectral cameras and a thermal infrared camera in order to select the most appropriate wavelengths and combinations thereof for best revealing high moisture areas, which are taken as a proxy for water leakage. The Temperature-Vegetation-Index method (T-VI, also known as Triangle/Trapezoid method) was found to provide the highest contrast-to-noise ratio. This preliminary work helped select the most appropriate onboard instrumentation for two types of aerial platforms, manned (MAV) and unmanned (UAV). Afterwards, a series of measurement campaigns were performed from 2017 to 2019 in an operational environment over two water distribution networks in France and Portugal. Artificial leaks were introduced and both remote sensing platforms successfully detected them when excluding the unfavorable situations of a recent rain event or high vegetation presence. With the most recent equipment configuration, known and unknown real leaks in the overflown part of a water transportation network in Portugal have been detected. A significant number of false alarms were also observed which were due either to natural water flows (groundwater exfiltration, irrigation runoff and ponds) or to vegetation-cover variability nearby water-distribution nodes. Close interaction with the water utilities, and ancillary information like topographic factors (e.g., slope orientation), are expected to reduce the false alarm rates and improve WADI's methodology performance.

Learning dynamic Bayesian networks from time-dependent and time-independent data: Unraveling disease progression in Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease causing patients to quickly lose motor neurons. The disease is characterized by a fast functional impairment and ventilatory decline, leading most patients to die from respiratory failure. To estimate when patients should get ventilatory support, it is helpful to adequately profile the disease progression. For this purpose, we use dynamic Bayesian networks (DBNs), a machine learning model, that graphically represents the conditional dependencies among variables. However, the standard DBN framework only includes dynamic (time-dependent) variables, while most ALS datasets have dynamic and static (time-independent) observations. Therefore, we propose the sdtDBN framework, which learns optimal DBNs with static and dynamic variables. Besides learning DBNs from data, with polynomial-time complexity in the number of variables, the proposed framework enables the user to insert prior knowledge and to make inference in the learned DBNs. We use sdtDBNs to study the progression of 1214 patients from a Portuguese ALS dataset. First, we predict the values of every functional indicator in the patients' consultations, achieving results competitive with state-of-the-art studies. Then, we determine the influence of each variable in patients' decline before and after getting ventilatory support. This insightful information can lead clinicians to pay particular attention to specific variables when evaluating the patients, thus improving prognosis. The case study with ALS shows that sdtDBNs are a promising predictive and descriptive tool, which can also be applied to assess the progression of other diseases, given time-dependent and time-independent clinical observations.

Phytol, a Chlorophyll Component, Produces Antihyperalgesic, Anti-inflammatory, and Antiarthritic Effects: Possible NFκB Pathway Involvement and Reduced Levels of the Proinflammatory Cytokines TNF-α and IL-6.

Phytol is a diterpene constituent of chlorophyll and has been shown to have several pharmacological properties, particularly in relation to the management of painful inflammatory diseases. Arthritis is one of the most common of these inflammatory diseases, mainly affecting the synovial membrane, cartilage, and bone in joints. Proinflammatory cytokines, such as TNF-α and IL-6, and the NFκB signaling pathway play a pivotal role in arthritis. However, as the mechanisms of action of phytol and its ability to reduce the levels of these cytokines are poorly understood, we decided to investigate its pharmacological effects using a mouse model of complete Freund's adjuvant (CFA)-induced arthritis. Our results showed that phytol was able to inhibit joint swelling and hyperalgesia throughout the whole treatment period. Moreover, phytol reduced myeloperoxidase (MPO) activity and proinflammatory cytokine release in synovial fluid and decreased IL-6 production as well as the COX-2 immunocontent in the spinal cord. It also downregulated the p38MAPK and NFκB signaling pathways. Therefore, our findings demonstrated that phytol can be an innovative antiarthritic agent due to its capacity to attenuate inflammatory reactions in joints and the spinal cord, mainly through the modulation of mediators that are key to the establishment of arthritic pain.

Correction to: In vitro mechanistic studies on α-amanitin and its putative antidotes.

In the original publication of the article.

In vitro mechanistic studies on α-amanitin and its putative antidotes.

α-Amanitin plays a key role in Amanita phalloides intoxications. The liver is a major target of α-amanitin toxicity, and while RNA polymerase II (RNA Pol II) transcription inhibition is a well-acknowledged mechanism of α-amanitin toxicity, other possible toxicological pathways remain to be elucidated. This study aimed to assess the mechanisms of α-amanitin hepatotoxicity in HepG2 cells. The putative protective effects of postulated antidotes were also tested in this cell model and in permeabilized HeLa cells. α-Amanitin (0.1-20 µM) displayed time- and concentration-dependent cytotoxicity, when evaluated through the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) reduction and neutral red uptake assays. Additionally, α-amanitin decreased nascent RNA synthesis in a concentration- and time-dependent manner. While α-amanitin did not induce changes in mitochondrial membrane potential, it caused a significant increase in intracellular ATP levels, which was not prevented by incubation with oligomycin, an ATP synthetase inhibitor. Concerning the cell redox status, α-amanitin did not increase reactive species production, but caused a significant increase in total and reduced glutathione, which was abolished by pre-incubation with the inhibitor of gamma-glutamylcysteine synthase, buthionine sulfoximine. None of the tested antidotes [N-acetyl cysteine, silibinin, benzylpenicillin, and polymyxin B (PolB)] conferred any protection against α-amanitin-induced cytotoxicity in HepG2 cells or reversed the inhibition of nascent RNA caused by the toxin in permeabilized HeLa cells. Still, PolB interfered with RNA Pol II activity at high concentrations, though not impacting on α-amanitin observed cytotoxicity. New hepatotoxic mechanisms of α-amanitin were described herein, but the lack of protection observed in clinically used antidotes may reflect the lack of knowledge on their true protection mechanisms and may explain their relatively low clinical efficacy.

The Role of Oxygen Atoms on Excitons at the Edges of Monolayer WS2.

We clarify that the chemisorption of oxygen atoms at the edges is a key contributor to the frequently observed edge enhancement and spatial non-uniformities of photoluminescence (PL) in WS2 monolayers. Here we have investigated with momentum- and real-space nanoimaging of the chemical and electronic density inhomogeneity of WS2 flakes. Our finding from a large panoply of techniques together with density functional theory calculation confirms that the oxygen chemisorption leads to the electron accumulation at the edges. This facilitates the trion dominance of PL at the edges of WS2 flakes. Our results highlight and unravel the significance of chemisorbed oxygen at the edges in the PL emission and electronic structure of WS2, providing a viable path to enhance the performance of transition-metal-dichalcogenide-based devices.

SLMP53-1 Inhibits Tumor Cell Growth through Regulation of Glucose Metabolism and Angiogenesis in a P53-Dependent Manner.

The Warburg effect is an emerging hallmark of cancer, which has the tumor suppressor p53 as its major regulator. Herein, we unveiled that p53 activation by (S)-tryptophanol-derived oxazoloisoindolinone (SLMP53-1) mediated the reprograming of glucose metabolism in cancer cells and xenograft human tumor tissue, interfering with angiogenesis and migration. Particularly, we showed that SLMP53-1 regulated glycolysis by downregulating glucose transporter 1 (GLUT1), hexokinase-2 (HK2), and phosphofructokinase-2 isoform 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase-3 (PFKFB3) (key glycolytic enzymes), while upregulating the mitochondrial markers synthesis of cytochrome c oxidase 2 (SCO2), cytochrome c oxidase subunit 4 (COX4), and OXPHOS mitochondrial complexes. SLMP53-1 also downregulated the monocarboxylate transporter 4 (MCT4), causing the subsequent reduction of lactate export by cancer cells. Besides the acidification of the extracellular environment, SLMP53-1 further increased E-cadherin and reduced metalloproteinase-9 (MMP-9) expression levels in both cancer cells and xenograft human tumor tissue, which suggested the interference of SLMP53-1 in extracellular matrix remodeling and epithelial-to-mesenchymal transition. Consistently, SLMP53-1 depleted angiogenesis, decreasing endothelial cell tube formation and vascular endothelial growth factor (VEGF) expression levels. SLMP53-1 also exhibited synergistic growth inhibitory activity in combination with the metabolic modulator dichloroacetic acid. These data reinforce the promising application of the p53-activating agent SLMP53-1 in cancer therapy, by targeting p53-mediated pathways of growth and dissemination.

AliClu - Temporal sequence alignment for clustering longitudinal clinical data.

BACKGROUND: Patient stratification is a critical task in clinical decision making since it can allow physicians to choose treatments in a personalized way. Given the increasing availability of electronic medical records (EMRs) with longitudinal data, one crucial problem is how to efficiently cluster the patients based on the temporal information from medical appointments. In this work, we propose applying the Temporal Needleman-Wunsch (TNW) algorithm to align discrete sequences with the transition time information between symbols. These symbols may correspond to a patient's current therapy, their overall health status, or any other discrete state. The transition time information represents the duration of each of those states. The obtained TNW pairwise scores are then used to perform hierarchical clustering. To find the best number of clusters and assess their stability, a resampling technique is applied. RESULTS: We propose the AliClu, a novel tool for clustering temporal clinical data based on the TNW algorithm coupled with clustering validity assessments through bootstrapping. The AliClu was applied for the analysis of the rheumatoid arthritis EMRs obtained from the Portuguese database of rheumatologic patient visits (Reuma.pt). In particular, the AliClu was used for the analysis of therapy switches, which were coded as letters corresponding to biologic drugs and included their durations before each change occurred. The obtained optimized clusters allow one to stratify the patients based on their temporal therapy profiles and to support the identification of common features for those groups. CONCLUSIONS: The AliClu is a promising computational strategy to analyse longitudinal patient data by providing validated clusters and by unravelling the patterns that exist in clinical outcomes. Patient stratification is performed in an automatic or semi-automatic way, allowing one to tune the alignment, clustering, and validation parameters. The AliClu is freely available at https://github.com/sysbiomed/AliClu.

Modelling cancer outcomes of bone metastatic patients: combining survival data with N-Telopeptide of type I collagen (NTX) dynamics through joint models.

BACKGROUND: Joint models (JM) have emerged as a promising statistical framework to concurrently analyse survival data and multiple longitudinal responses. This is particularly relevant in clinical studies where the goal is to estimate the association between time-to-event data and the biomarkers evolution. In the context of oncological data, JM can indeed provide interesting prognostic markers for the event under study and thus support clinical decisions and treatment choices. However, several problems arise when dealing with this type of data, such as the high-dimensionality of the covariates space, the lack of knowledge about the function structure of the time series and the presence of missing data, facts that may hamper the accurate estimation of the JM. METHODS: We propose to apply JM for the analysis of bone metastatic patients and infer the association of their survival with several covariates, in particular the N-Telopeptide of Type I Collagen (NTX) dynamics. This biomarker has been identified as a relevant prognostic factor in patients with metastatic cancer, but only using static information in some specific time points. RESULTS: We extended this analysis using the full NTX time series for a larger cohort of patients with bone metastasis, and compared the results obtained by the JM and the extended Cox regression model. Imputation based on fuzzy clustering was used to deal with missing values and several functions for NTX evolution were compared, such as rational, exponential and cubic splines. CONCLUSIONS: The JM obtained confirm the association between NTX values and patients' response, attesting the importance of this time series, and additionally provide a deep understanding of the key survival covariates.

Information-Theoretical Criteria for Characterizing the Earliness of Time-Series Data.

Biomedical signals constitute time-series that sustain machine learning techniques to achieve classification. These signals are complex with measurements of several features over, eventually, an extended period. Characterizing whether the data can anticipate prediction is an essential task in time-series mining. The ability to obtain information in advance by having early knowledge about a specific event may be of great utility in many areas. Early classification arises as an extension of the time-series classification problem, given the need to obtain a reliable prediction as soon as possible. In this work, we propose an information-theoretic method, named Multivariate Correlations for Early Classification (MCEC), to characterize the early classification opportunity of a time-series. Experimental validation is performed on synthetic and benchmark data, confirming the ability of the MCEC algorithm to perform a trade-off between accuracy and earliness in a wide-spectrum of time-series data, such as those collected from sensors, images, spectrographs, and electrocardiograms.

Why the Flavin Adenine Dinucleotide (FAD) Cofactor Needs To Be Covalently Linked to Complex†II of the Electron-Transport Chain for the Conversion of FADH2 into FAD.

A covalently bound flavin cofactor is predominant in the succinate-ubiquinone oxidoreductase (SQR; Complex II), an essential component of aerobic electron transport, and in the menaquinol-fumarate oxidoreductase (QFR), the anaerobic counterpart, although it is only present in approximately 10 % of the known flavoenzymes. This work investigates the role of this 8α-N3-histidyl linkage between the flavin adenine dinucleotide (FAD) cofactor and the respiratory Complex II. After parameterization with DFT calculations, classical molecular-dynamics simulations and quantum-mechanics calculations for Complex II:FAD and Complex II:FADH2 , with and without the covalent bond, were performed. It was observed that the covalent bond is essential for the active-center arrangement of the FADH2 /FAD cofactor. Removal of this bond causes a displacement of the isoalloxazine group, which influences interactions with the protein, flavin solvation, and possible proton-transfer pathways. Specifically, for the noncovalently bound FADH2 cofactor, the N1 atom moves away from the His-A365 and His-A254 residues and the N5 atom moves away from the glutamine-62A residue. Both of the histidine and glutamine residues interact with a chain of water molecules that cross the enzyme, which is most likely involved in proton transfer. Breaking this chain of water molecules could thereby compromise proton transfer across the two active sites of Complex II.