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ABSTRACTThe Maps of Geohazard features of the Western Sardinia produced by the Magic project (MArine Geohazard along Italian Coasts) were presented. The maps were created using morphobathymetric data obtained by surveying with a multi-beam echo sounder. The main potential geo-hazards on the western Sardinian margin are related with the presence of three pockmark fields which include hundreds of elements from few meters to several hundred meters in diameter. The pockmark fields are located ∼25–35 km off the coastline and at more than 400 m depth.

Samarium doped (0.5–5 at.%) ZrO2 films with high surface and optical quality were obtained by sol–gel method using inorganic zirconium precursor. The morphology and roughness of the samples were investigated by scanning electron microscopy (SEM) and atomic force microscopy (AFM). A phase transition following the doping with Sm3+ ions was registered by the X-ray diffraction (XRD) method, leading to stabilization of the tetragonal ZrO2 phase. Optical parameters such as refractive index and extinction coefficient were determined for each sample. X-ray photoelectron spectroscopy (XPS) revealed the presence of carbon impurities as well as a shift of the valence band (VB) edge to lower binding energies with the increase of the dopant level. The electron paramagnetic resonance (EPR) proved the presence of oxygen vacancies and other defects (Zr3+, T-centers) in the structure of the films which prompted photoluminescence (PL) in the material. The emission spectra of the pure ZrO2 and Sm-doped samples revealed the beneficial effect of the host matrix, capable of boosting the characteristic orange-red emission of the Sm3+ ions. The obtained films have very high ratio of acceptor/donor emission intensity (Ia/Id = 25) making them promising candidates for applications in the field of photonics, including in the development of white LEDs.

Research on the professional satisfaction of doctors is especially relevant today, in relation to the crisis with medical staff. The purpose of this article is to study the factors of job satisfaction of general practitioners. An empirical sociological survey was conducted among general practitioners in the Stara Zagora region. The method used is a face-to-face interview. The approved Warr-Cook-Wall (WCW) toolkit is implemented - a questionnaire modified by the research team. The study includes 223 general practitioners, which determines the high responsiveness: 94.9%. The results show that GPs are mostly satisfied with the factors related to the autonomy of the profession. The clinical freedom defined as the ability of general practitioners to choose the method of treatment of their patients is assessed to the greatest extent as satisfactory. Physicians are most dissatisfied with the regulatory framework they have to comply with and with the constant change of activities required in their daily practice. The questionnaire for the assessment of job satisfaction of GPs is applied for the first time among Bulgarian physicians. This assessment is useful for the formation of policies for human resource management in health care, as it identifies adverse trends and leads to certain solutions. Models of professional satisfaction influence the professional behavior, important for staying and leaving certain medical area and the emerging shortage of doctors in some specialties.

In this paper, a solution for vehicle speed estimation using unmanned aerial videos is described. First, convolutional neural networks and Kalman filtering using deep features are used for detecting and tracking vehicles. Then, a photogrammetric approach is developed for estimating the three-dimensional (3D) position of the tracked vehicles on the road, which allows determining their speed. No assumptions are made about either the 3D structure of the road (e.g., constraining it to be a planar surface) or the camera pose (e.g., restricting it to be stationary). Therefore, this solution applies to videos acquired by a moving unmanned aerial vehicle from complex road structures (e.g., multi-level highways). This solution is also robust to changes of viewpoint and scale, which makes it applicable to situations where cars undergo orientation and resolution changes as observed from the sky (e.g., in roundabouts). Experiments showed that a high detection accuracy could be achieved with an F1-score of 94.54%. Besides, the tracking technique performed well, with a multiple-object tracking accuracy of 89.8% at a speed of 11 frames per second on videos of 2720×1530 pixels. Vehicle positioning (and thus, speed estimation) could be performed with an average accuracy of 0.6 m.

The expected global sea level rise by the year 2100 will determine adaptation of the whole coastal system and the land retreat of the shoreline. Future scenarios coupled with the improvement of mining technologies will favour increased exploitation of sand deposits for nourishment, especially for urban beaches and sandy coasts with lowlands behind them. The objective of the work is to provide useful tools to support planning in the management of sand deposits located on the continental shelf of Western Sardinia (western Mediterranean Sea). The work has been realised through the integration of data and information collected during several projects. Available data consist of morpho-bathymetric data (multibeam) associated with morphoacoustic (backscatter) data, collected in the depth range −25 to −700 m. Extensive coverage of high-resolution seismic profiles (Chirp 3.5 kHz) has been acquired along the continental shelf. Also, surface sediment samples (Van Veen grab and box corer) and vibrocorers have been collected. These data allow mapping of the submerged sand deposits with the determination of their thickness and volume and their sedimentological characteristics. Furthermore, it is possible to map the seabed geomorphological features of the continental shelf of Western Sardinia. All the available data (https://doi.org/10.1594/PANGAEA.895430) have been integrated and organised in a geodatabase implemented through a GIS and the software suite Geoinformation Enabling ToolkIT StarterKit® (GET-IT), developed by researchers of the Italian National Research Council for RITMARE project. GET-IT facilitates the creation of distributed nodes of an interoperable spatial data infrastructure (SDI) and enables unskilled researchers from various scientific domains to create their own Open Geospatial Consortium (OGC) standard services for distributing geospatial data, observations and metadata of sensors and data sets. Data distribution through standard services follows the guidelines of the European Directive INSPIRE (DIRECTIVE 2007/2/EC); in particular, standard metadata describe each map level, containing identifiers such as data type, origin, property, quality, processing processes to foster data searching and quality assessment.

Coastal ecosystems produce and store carbonate particles, which play a significant role in the carbonate dynamics of coastal areas and may contribute to the sediment budget of adjacent beaches. In the nearshore seabed of temperate zones (e.g. Mediterranean Sea and South Australia), marine biogenic carbonates are mainly produced inside seagrass meadows. This study quantifies the contribution of biogenic sediments, mainly produced in Posidonia oceanica seagrass meadows and secondarily in photophilic algal communities, to the sediment budget of a Mediterranean beach–dune system (San Giovanni beach, western Sardinia, western Mediterranean Sea). A set of geophysical, petrographic and sedimentological data was used to estimate the sediment volume and composition of the beach–dune system as a whole. The San Giovanni beach–dune system contains 3 797 000 ± 404 000 t of sediment, 83 % (3 137 000 ± 404 000 t) of which is located in the coastal wedge, 16 % (619 000 ± 88 000 t) in the dune fields and 1 % (41 000 ± 15 000 t) in the subaerial beach. The sediments are composed of mixed modern bioclastic and relict bioclastic and non-bioclastic grains from various sources. The system receives a large input of modern bioclastic grains, mainly composed of rhodophytes, molluscs and bryozoans, which derive from sediment production of present-day carbonate factories, particularly P. oceanica seagrass meadows. Radiocarbon dating of modern bioclastic grains indicated that they were produced during the last 4.37 kyr. This value was used to estimate the long-term deposition rates of modern bioclastic sediments in the various beach compartments. The total deposition rate of modern bioclastic grains is 46 000 ± 5000 t century−1, mainly deposited in the coastal wedge (39 000 ± 4 000 t century−1) and dunes (7000 ± 1000 t century−1), and 46 000 t represents ∼ 1.2 % of the total beach–dune sediment mass. Carbonate production from coastal ecosystems was estimated to be 132 000∕307 000 t century−1, 28 % (15 % ∕ 34 %) of which is transported to the beach–dune system, thus significantly contributing to the beach sediment budget. The contribution to the beach sediment budget represents a further ecosystem service, which our data can help quantify, provided by P. oceanica. The value of this sediment-supply service is in addition to the other important ecological services provided by seagrass meadows. The dependence of the beach sediment budget on carbonate production associated with coastal ecosystems has several implications for the adaptation of mixed and carbonate beaches to the loss of seagrass meadows due to local impacts and the changes expected to occur over the next few decades in coastal ecosystems following sea level rise.

The presence and distribution of harmful algal species were investigated along the coasts of Sardinia in the summer of 2012. Fourteen potentially noxious taxa were identified at 74 beaches. The majority of the recovered taxa were potentially toxic and/or high biomass producers. Alexandrium taylorii, Gymnodinium instriatum, and Ostreopsis cf. ovata were the most frequent and abundant taxa, although Barrufeta bravensis reached the highest density (4.4 × 106 cells L−1). Barrufeta bravensis, A. taylorii, and G. instriatum were responsible for intense water discoloration at two of the beaches sampled. Polymerase chain reaction (PCR) analyses supported the identification of several taxa and decisively identified B. bravensis. PCR assays increased the information available on the species distributions. The locations studied were heterogeneous in their prevailing environmental conditions and their morphodynamic profiles. Statistical analyses indicated that the distributions of harmful algal species correlated with gravel and medium-fine sand substrata. These data provide substantial knowledge on the distributions of harmful algal species on beaches, which have been poorly studied on a global scale. The apparent relationship between noxious species and grain size suggests that vegetative cells may be recruited from cyst beds in beach sediments.

A procedure is developed for the study of wear of aluminum alloys AlSi7 obtained by casting, reinforced by TiC microparticles, before and after heat treatment. Tribological study is realized under conditions of friction on counterbody with fixed abrasive. Experimental results were obtained for mass wear, wear rate, wear intensity and wear-resistance of the alloys with different wt% of microparticles.

Since 2007 Bulgaria and Romania are members of the European Union. All member states have to introduce the European earthquake building code EUROCODE 8 (EC 8) in the coming years. Therefore, new seismic hazard maps have to be calculated according to the recommendations in EC 8. Here the authors present a novel approach to compute such hazard maps. We prefer to use the macroseismic intensity as hazard parameter because of two reasons: - The irregular azimuthal attenuation pattern of the Vrancea intermediate depth earthquakes can be advantageously taken into account by using detailed macroseismic maps. - The intensity is directly related to the degree of damage and is the original information in the historical earthquake catalogues. The main base of our probabilistic analysis is the earthquake catalogue for SE-Europe (Shebalin et al., 1998) in combination with national and regional catalogues. Fore- and aftershocks were removed. Seismic source zones inside an area of about 200 km around Romania and Bulgaria were defined based on seismicity, neotectonics and geological development. For each seismic source the intensity-frequency relation was calculated and a maximum possible earthquake as well as a seismogenic depth was estimated. An appropriate attenuation law was assumed. To cope with the irregular isoseismals of the Vrancea intermediate depth earthquakes, a factor Ω was included in the macroseismic attenuation law. Using detailed macroseismic maps of three strong intermediate depth earthquakes, Ω was calculated for each observation. Strong local variations of Ω are avoided by averaging within grid cells of 0.5 degree in longitude and 0.25 degree in latitude. The contributions of all seismic sources, the crustal normal depth source zones and the Vrancea intermediate depth zone, were summed up and the annual probability of exceedance was calculated. The contribution of the Vrancea intermediate depth zone to each grid point was computed with the corresponding representative Ω of this point; a seismogenic depth of 120 km has been assumed. Each final seismic hazard map is a combination of two maps, the one for normal depth source zones and the one for the Vrancea intermediate depth zone. This is illustrated for a recurrence period of 475 years. Additional hazard maps were calculated for different recurrence periods.

Nowadays manufacturers are facing rapid and fundamental changes in the ways business is done. Producers are looking for simulation systems increasing throughput and profit, reducing cycle time, improving due-date performance, reducing WIP, providing plant-wide synchronization, etc. Planning and scheduling of coffee production is important for the manufacturer to synchronize production capacity and material inputs to meet the delivery date promised to the customer. A simulation model of coffee production was compiled. It includes roasting, grinding and packaging processes. Using this model the basic features of the coffee production system are obtained. An optimization module of the simulation SW is used for improving the current structure of the production system. Gantt charts and reports are applied for scheduling. Capacity planning problems related to coffee production are discussed.

The capital of Bulgaria, Sofia, is situated in the center of the so-called Sofia area. This is the most populated industrial and cultural region of Bulgaria that faces considerable earthquake risk. We apply a version of machine code EQRISK for hazard assessment of the Sofia area according to the Cornell-McGuire approach. The probabilistic seismic hazard analysis is based on a simplified seismogenic model, which is derived from seismic zoning of Bulgaria. We show, using a Monte Carlo approach, that uncertainties in seismic input have a relatively small effect on the PSHA output, especially when compared with uncertainties associated with the attenuation relationship. Our PSHA map shows that a 103 annual probability of the PGA exceeds 0.3 g in much of the Sofia area

We apply statistical analysis to study the temporal distribution of aftershocks in aftershock sequences of five earthquakes which occurred in Bulgaria. We use the maximum likelihood method to estimate the parameters of the modified Omori formula for aftershock sequences which is directly based on a time series. We find that: the maximum likelihood estimates of the parameter p show a regional variation, with lower values of the decay rate in North Bulgaria; the modified Omori formula provides an appropriate representation of temporal variation of the aftershock activity in North Bulgaria; the aftershock sequences in South Bulgaria are best modeled by the combination of an ordinary aftershock sequence with secondary aftershock activity. A plot of the cumulative number of events versus the frequency-linearized time t clearly demonstrates a transition from aftershock to foreshock activity prior to the second 1986 Strazhitsa (North Bulgaria) earthquake.

According to the World Health Organization, the number of tuberculosis (TB) infections and the drug-resistant burden worldwide increased by 4.5% and 3.0%, respectively, between 2020 and 2021. Disease severity and complexity drive the interest for undertaking new clinical trials to provide efficient treatment to limit spread and drug resistance. TB Alliance conducted a phase 2 study in 106 patients to guide linezolid (LZD) dose selection using early bactericidal activity over 14 days of treatment. LZD is highly efficient for drug-resistant TB treatment, but treatment monitoring is required since serious adverse events can occur. The objective of this study was to develop a pharmacokinetic-pharmacodynamic (PKPD) model to analyze the dose-response relationship between linezolid exposure and efficacy biomarkers. Using time to positivity (TTP) and colony-forming unit (CFU) count data, we developed a PKPD model in six dosing regimens, differing on LZD dosing intensity. A one-compartment model with five transit absorption compartments and non-linear auto-inhibition elimination described best LZD pharmacokinetic characteristics. TTP and CFU logarithmic scaled [log(CFU)] showed a bactericidal activity of LZD against Mycobacterium tuberculosis . TTP was defined by a model with two significant covariates: the presence of uni- and bilateral cavities decreased baseline TTP value by 24%, and an increase on every 500 mg/L/h of cumulative area under the curve increased the rate at which TTP and CFU change from baseline by 20% and 11%, respectively., Clinical Trials: This study is registered with ClinicalTrials.gov as NCT02279875., Competing Interests: The authors declare no conflict of interest.

Purpose: To compare the effectiveness and safety of cefazolin versus cloxacillin for the treatment of infective endocarditis (IE) due to methicillin-sensitive Staphylococci (MSS)., Methods: Data were retrospectively collected on patients treated for a definite MSS endocarditis who received cefazolin or cloxacillin for at least 10 consecutive days in six French hospitals between January-1 2014 and December-31 2020. The primary endpoint was treatment failure defined as a composite of death within 90 days of starting antibiotherapy, or embolic event during antibiotherapy, or relapse of IE within 90 days of stopping antibiotherapy. We used Cox regression adjusted for the inverse probability of treatment weighting of receiving cefazolin., Results: 192 patients were included (median age 67.8 years). IE was caused by S.aureus in 175 (91.1%) and by coagulase-negative staphylococci in 17 (8.9%). Ninety-four patients (48.9%) received cefazolin, and 98 (51%) received cloxacillin. 34 patients (34.7%) with cefazolin and 26 (27.7%) with cloxacillin met the composite primary endpoint, with no significant differences between groups (adjusted HR = 1.13, 95% CI 0.63 to 2.03). There were no significant differences in secondary efficacy endpoints or biological safety events., Conclusion: The effectiveness of cefazolin did not significantly differ from cloxacillin for the treatment of MSS endocarditis., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Introduction: Small-molecule Programmable Cell Death Protein 1/Programmable Death-Ligand 1 (PD1/PDL1) inhibition via PDL1 dimerization has the potential to lead to inexpensive drugs with better cancer patient outcomes and milder side effects. However, this therapeutic approach has proven challenging, with only one PDL1 dimerizer reaching early clinical trials so far. There is hence a need for fast and accurate methods to develop alternative PDL1 dimerizers., Objectives: We aim to show that structure-based virtual screening (SBVS) based on PDL1-specific machine-learning (ML) scoring functions (SFs) is a powerful drug design tool for detecting PD1/PDL1 inhibitors via PDL1 dimerization., Methods: By incorporating the latest MLSF advances, we generated and evaluated PDL1-specific MLSFs (classifiers and inactive-enriched regressors) on two demanding test sets., Results: 60 PDL1-specific MLSFs (30 classifiers and 30 regressors) were generated. Our large-scale analysis provides highly predictive PDL1-specific MLSFs that benefitted from training with large volumes of docked inactives and enabling inactive-enriched regression., Conclusion: PDL1-specific MLSFs strongly outperformed generic SFs of various types on this target and are released here without restrictions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Production and hosting by Elsevier B.V.)

Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in size. Artificial intelligence and, more concretely, its machine-learning (ML) branch, including deep learning, have effectively exploited these data sets to build scoring functions (SFs) for SBVS against targets with an atomic-resolution 3D model (e.g., generated by X-ray crystallography or predicted by AlphaFold2). Often outperforming their generic and non-ML counterparts, target-specific ML-based SFs represent the state of the art for SBVS. Here, we present a comprehensive and user-friendly protocol to build and rigorously evaluate these new SFs for SBVS. This protocol is organized into four sections: (i) using a public benchmark of a given target to evaluate an existing generic SF; (ii) preparing experimental data for a target from public repositories; (iii) partitioning data into a training set and a test set for subsequent target-specific ML modeling; and (iv) generating and evaluating target-specific ML SFs by using the prepared training-test partitions. All necessary code and input/output data related to three example targets (acetylcholinesterase, HMG-CoA reductase, and peroxisome proliferator-activated receptor-α) are available at https://github.com/vktrannguyen/MLSF-protocol , can be run by using a single computer within 1 week and make use of easily accessible software/programs (e.g., Smina, CNN-Score, RF-Score-VS and DeepCoy) and web resources. Our aim is to provide practical guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules within a given compound library., (© 2023. Springer Nature Limited.)

The interaction between PD1 and its ligand PDL1 has been shown to render tumor cells resistant to apoptosis and promote tumor progression. An innovative mechanism to inhibit the PD1/PDL1 interaction is PDL1 dimerization induced by small-molecule PDL1 binders. Structure-based virtual screening is a promising approach to discovering such small-molecule PD1/PDL1 inhibitors. Here we investigate which type of generic scoring functions is most suitable to tackle this problem. We consider CNN-Score, an ensemble of convolutional neural networks, as the representative of machine-learning scoring functions. We also evaluate Smina, a commonly used classical scoring function, and IFP, a top structural fingerprint similarity scoring function. These three types of scoring functions were evaluated on two test sets sharing the same set of small-molecule PD1/PDL1 inhibitors, but using different types of inactives: either true inactives (molecules with no in vitro PD1/PDL1 inhibition activity) or assumed inactives (property-matched decoy molecules generated from each active). On both test sets, CNN-Score performed much better than Smina, which in turn strongly outperformed IFP. The fact that the latter was the case, despite precluding any possibility of exploiting decoy bias, demonstrates the predictive value of CNN-Score for PDL1. These results suggest that re-scoring Smina-docked molecules with CNN-Score is a promising structure-based virtual screening method to discover new small-molecule inhibitors of this therapeutic target., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Viet-Khoa Tran-Nguyen reports financial support was provided by 10.13039/501100004097Fondation ARC pour la Recherche sur le Cancer (ARC). Pedro J. Ballester reports financial support was provided by 10.13039/501100001852Indo-French Centre for the Promotion of Advanced Research. Pedro J. Ballester reports financial support was provided by 10.13039/501100001665French National Research Agency. Pedro J. Ballester reports financial support was provided by 10.13039/501100004097Fondation ARC pour la Recherche sur le Cancer (ARC)., (© 2022 The Authors.)

Introduction: Stereotactic MR-guided adaptive radiotherapy (SMART) is an attractive modality of radiotherapy for pancreatic tumors. The objectives of this prospective registry study were to report the dosimetric benefits of daily adaptation of SMART and the first clinical results in pancreatic tumors., Materials and Methods: All patients treated in our center with SMART for a pancreatic tumor were included. Patients were planned for five daily-adapted fractions on consecutive days. Endpoints were acute toxicities, late toxicities, impact of adaptive treatment on target volume coverage and organs at risk (OAR) sparing, local control (LC) rate, distant metastasis-free survival (DMFS), and overall survival (OS)., Results: Thirty consecutive patients were included between October 2019 and April 2021. The median dose prescription was 50 Gy. No patient presented grade > 2 acute toxicities. The most frequent grade 1-2 toxicities were asthenia (40%), abdominal pain (40%), and nausea (43%). Daily adaptation significantly improved planning target volume (PTV) and gross tumor volume (GTV) coverage and OAR sparing. With a median follow-up of 9.7 months, the median OS, 6-month OS, and 1-year OS were 14.1 months, 89% (95% CI: 70%-96%), and 75% (95% CI: 51%-88%), respectively, from SMART completion. LC at 6 months and 1 year was respectively 97% (95% CI: 79-99.5%) and 86% (95% CI: 61%-95%). There were no grade > 2 late toxicities. With a median follow-up of 10.64 months, locally advanced pancreatic cancer (LAPC) and borderline resectable pancreatic cancer (BRPC) patients (22 patients) had a median OS, 6-month OS, and 1-year OS from SMART completion of 14.1 months, 76% (95% CI: 51%-89%), and 70% (95% CI: 45%-85%), respectively. Nine patients underwent surgical resection (42.1% of patients with initial LAPC and 33.3% of patients with BRPC), with negative margins (R0). Resected patients had a significantly better OS as compared to unresected patients (p = 0.0219, hazard ratio (HR) = 5.78 (95% CI: 1.29-25.9))., Conclusion: SMART for pancreatic tumors is feasible without limiting toxicities. Daily adaptation demonstrated a benefit for tumor coverage and OAR sparing. The severity of observed acute and late toxicities was low. OS and LC rates were promising. SMART achieved a high secondary resection rate in LAPC patients. Surgery after SMART seemed to be feasible and might increase OS in these patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Michalet, Bordeau, Cantaloube, Valdenaire, Debuire, Simeon, Portales, Draghici, Ychou, Assenat, Dupuy, Gourgou, Colombo, Carrere, Souche, Aillères, Fenoglietto, Azria and Riou.)

Objectives: To compare the efficacy of temocillin with carbapenems for extended spectrum β-lactamase (ESBL)-producing Enterobacteriaceae urinary tract infections (ESBL-E UTI)., Methods: A multicenter retrospective case-control study of adults with ESBL-E UTI was conducted between January 2015 and October 2019. Cases received temocillin ≥ 50% of the effective antibiotic therapy duration and controls exclusively received carbapenem; they were statistically matched (1:1 ratio) on 6-month period, sex and age. The clinical cure at the end of antibiotic therapy was analysed using conditional logistic regression., Results: Seventy-two temocillin cases were matched to 72 carbapenem controls. Most (67%) were male, median age was 69.4 years, 81 (56%) were immunocompromised, including 44 (31%) solid organ transplant recipients. There was no difference between cases and controls for baseline characteristics and microorganisms involved: Klebsiella pneumoniae in 59 (41%), Escherichia coli in 57 (40%), and Enterobacter spp. in 24 (17%). The median time from admission to effective antibiotic therapy was 0 days [range, 0-2]. Among cases, first-line antibiotic therapy (≤ 72 hours) was temocillin in six (8%) and carbapenems in 39 (54%). Temocillin was given at the median daily dose of 4 g [range, 2-4] after 3 days [range, 2-5] of carbapenems. Patients received temocillin for 81% [range, 70-93] of the effective antibiotic course duration over 11 days [range, 8-14]. The effective antibiotic duration was similar in cases and controls (P = 0.067). Clinical cure at the end of antibiotic therapy was 94% (68/72) in cases vs. 99% (71/72) in controls (P = 0.206), with no difference among immunocompromised and solid organ transplant patients (P > 0.050)., Conclusions: Temocillin effectively relayed β-lactams, including carbapenems, to treat (complicated) ESBL-E UTI. Its efficacy was consistent among kidney transplant recipients., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background: Despite continued efforts to develop new treatments, there is an urgent need to discover new drug leads to treat tumors exhibiting primary or secondary resistance to existing drugs. Cell cultures derived from patient-derived orthotopic xenografts are promising pre-clinical models to better predict drug response in cancer recurrence., Objective: The aim of the study was to investigate the relationship between the physiochemical properties of drugs and their in vitro potency as well as identifying chemical scaffolds biasedtowards selectivity or promiscuity of such drugs., Methods: The bioactivities of 158 drugs screened against cell cultures derived from 30 cancer orthotopic patient-derived xenograft (O-PDX) models were considered. Drugs were represented by physicochemical descriptors and chemical structure fingerprints. Supervised learning was employed to model the relationship between features and in vitro potency., Results: Drugs with in vitro potency for alveolar rhabdomyosarcoma and osteosarcoma tend to have a higher number of rings, two carbon-hetero bonds and halogens. Selective and promiscuous scaffolds for these phenotypic targets were identified. Highly-predictive models of in vitro potency were obtained across these 30 targets, which can be applied to unseen molecules via a webserver (https://rnewbie.shinyapps.io/Shobek-master)., Conclusion: It is possible to identify privileged chemical scaffolds and predict the in vitro potency of unseen molecules across these 30 targets This information and models should be helpful to select which molecules to screen against these primary cultures of pediatric solid tumors., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Zymomonas mobilis is an ethanologenic microbe that has a demonstrated potential for use in lignocellulosic biorefineries for bioethanol production. Z. mobilis exhibits a number of desirable characteristics for use as an ethanologenic microbe, with capabilities for metabolic engineering and bioprocess modification. Many advanced genetic tools, including mutation techniques, screening methods and genome editing have been successively performed to improve various Z. mobilis strains as potential consolidated ethanologenic microbes. Many bioprocess strategies have also been applied to this organism for bioethanol production. Z. mobilis biofilm reactors have been modified with various benefits, including high bacterial populations, less fermentation times, high productivity, high cell stability, resistance to the high concentration of substrates and toxicity, and higher product recovery. We suggest that Z. mobilis biofilm reactors could be used in bioethanol production using lignocellulosic substrates under batch, continuous and repeated batch processes.

The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.

Janus kinase 2 (JAK2) inhibitors represent a promising therapeutic class of anticancer agents against many myeloproliferative disorders. Bioactivity data on pIC 50 of 2229 JAK2 inhibitors were employed in the construction of quantitative structure-activity relationship (QSAR) models. The models were built from 100 data splits using decision tree (DT), support vector machine (SVM), deep neural network (DNN) and random forest (RF). The predictive power of RF models were assessed via 10-fold cross validation, which afforded excellent predictive performance with R 2 and RMSE of 0.74 ± 0.05 and 0.63 ± 0.05, respectively. Moreover, test set has excellent performance of R 2 (0.75 ± 0.03) and RMSE (0.62 ± 0.04). In addition, Y-scrambling was utilized to evaluate the possibility of chance correlation of the predictive model. A thorough analysis of the substructure fingerprint count was conducted to provide insights on the inhibitory properties of JAK2 inhibitors. Molecular cluster analysis revealed that pyrazine scaffolds have nanomolar potency against JAK2.

Volume of distribution (Vd ss ) is a measure of how effectively a drug molecule is distributed throughout the body. Along with the clearance, it determines the half-life and therefore the drug dosing interval. A number of different pre-clinical approaches are available to predict the Vd ss in human including quantitative structure activity relationship (QSAR) models. Vd ss QSAR models have been reported for human and rat, but not important pre-clinical species including dog, mouse and monkey. In this study, we have generated Vd ss QSAR model on the human and commonly used pre-clinical species, each of which differs in terms of size, chemical diversity and data quality. We discuss the model performance by species, assess the effect the domain of applicability and the relative merits of building chemical series-specific models. In addition, we compare the intrinsic variability of the experimental logVd ss data (∼1.2 fold error) to in-vivo interspecies differences (∼2 fold error) and in silico based models (∼3 fold error). This prompted us to explore whether one species could be used to predict another, particularly where little data for that species is available. i. e. does the expansion in domain of applicability prove beneficial over and above any deterioration due to the use of response values from an alternative species., (© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Protein kinases are an important class of enzymes that play an essential role in virtually all major disease areas. In addition, they account for approximately 50% of the current targets pursued in drug discovery research. In this work, we explore the generation of structure-based quantum mechanical (QM) quantitative structure-activity relationship models (QSAR) as a means to facilitate structure-guided optimization of protein kinase inhibitors. We explore whether more accurate, interpretable QSAR models can be generated for a series of 76 N-phenylquinazolin-4-amine inhibitors of epidermal growth factor receptor (EGFR) kinase by comparing and contrasting them to other standard QSAR methodologies. The QM-based method involved molecular docking of inhibitors followed by their QM optimization within a ~ 300 atom cluster model of the EGFR active site at the M062X/6-31G(d,p) level. Pairwise computations of the interaction energies with each active site residue were performed. QSAR models were generated by splitting the datasets 75:25 into a training and test set followed by modelling using partial least squares (PLS). Additional QSAR models were generated using alignment dependent CoMFA and CoMSIA methods as well as alignment independent physicochemical, e-state indices and fingerprint descriptors. The structure-based QM-QSAR model displayed good performance on the training and test sets (r 2  ~ 0.7) and was demonstrably more predictive than the QSAR models built using other methods. The descriptor coefficients from the QM-QSAR models allowed for a detailed rationalization of the active site SAR, which has implications for subsequent design iterations.

Background: The management of breath-induced tumor motion is a major challenge for lung stereotactic body radiation therapy (SBRT). Three techniques are currently available for these treatments: tracking (T), gating (G) and free-breathing (FB)., Aim: To evaluate the dosimetric differences between these three treatment techniques for lung SBRT., Materials and Methods: Pretreatment 4DCT data were acquired for 10 patients and sorted into 10 phases of a breathing cycle, such as 0% and 50% phases defined respectively as the inhalation and exhalation maximum. GTV ph , PTV ph (=GTV ph  + 3 mm) and the ipsilateral lung were contoured on each phase.For the tracking technique, 9 fixed fields were adjusted to each PTV ph for the 10 phases. The gating technique was studied with 3 exhalation phases (40%, 50% and 60%). For the free-breathing technique, ITV FB was created from a sum of all GTV ph and a 3 mm margin was added to define a PTV FB . Fields were adjusted to PTV FB and dose distributions were calculated on the average intensity projection (AIP) CT. Then, the beam arrangement with the same monitor units was planned on each CT phase.The 3 modalities were evaluated using DVHs of each GTV ph , the homogeneity index and the volume of the ipsilateral lung receiving 20 Gy ( V 20Gy )., Results: The FB system improved the target coverage by increasing D mean (75.87 (T) -76.08 (G) -77.49 (FB) Gy). Target coverage was slightly more homogeneous, too (HI: 0.17 (T and G) -0.15 (FB) ). But the lung was better protected with the tracking system ( V 20Gy : 3.82 (T) -4.96 (G) -6.34 (FB) %)., Conclusions: Every technique provides plans with a good target coverage and lung protection. While irradiation with free-breathing increases doses to GTV, irradiation with the tracking technique spares better the lung but can dramatically increase the treatment complexity.

Immune therapy is generally seen as the future of cancer treatment. The discovery of tumor-associated antigens and cytotoxic T lymphocyte epitope peptides spurned intensive research into effective peptide-based cancer vaccines. One of the major obstacles hindering the development of peptide-based cancer vaccines is the lack of humoral response induction. As of now, very limited work has been performed to identify epitope peptides capable of inducing both cellular and humoral anticancer responses. In addition, no research has been carried out to analyze the structure and properties of peptides responsible for such immunological activities. This study utilizes a machine learning method together with interpretable descriptors in an attempt to identify parameters determining the immunotherapeutic activity of cancer epitope peptides.

Background: Currently, monomeric fluorescent proteins (FP) are ideal markers for protein tagging. The prediction of oligomeric states is helpful for enhancing live biomedical imaging. Computational prediction of FP oligomeric states can accelerate the effort of protein engineering efforts of creating monomeric FPs. To the best of our knowledge, this study represents the first computational model for predicting and analyzing FP oligomerization directly from the amino acid sequence., Results: After data curation, an exhaustive data set consisting of 397 non-redundant FP oligomeric states was compiled from the literature. Results from benchmarking of the protein descriptors revealed that the model built with amino acid composition descriptors was the top performing model with accuracy, sensitivity and specificity in excess of 80% and MCC greater than 0.6 for all three data subsets (e.g. training, tenfold cross-validation and external sets). The model provided insights on the important residues governing the oligomerization of FP. To maximize the benefit of the generated predictive model, it was implemented as a web server under the R programming environment., Conclusion: osFP affords a user-friendly interface that can be used to predict the oligomeric state of FP using the protein sequence. The advantage of osFP is that it is platform-independent meaning that it can be accessed via a web browser on any operating system and device. osFP is freely accessible at http://codes.bio/osfp/ while the source code and data set is provided on GitHub at https://github.com/chaninn/osFP/.Graphical Abstract.

Alzheimer's disease (AD) is a chronic neurodegenerative disease which leads to the gradual loss of neuronal cells. Several hypotheses for AD exists (e.g., cholinergic, amyloid, tau hypotheses, etc.). As per the cholinergic hypothesis, the deficiency of choline is responsible for AD; therefore, the inhibition of AChE is a lucrative therapeutic strategy for the treatment of AD. Acetylcholinesterase (AChE) is an enzyme that catalyzes the breakdown of the neurotransmitter acetylcholine that is essential for cognition and memory. A large non-redundant data set of 2,570 compounds with reported IC50 values against AChE was obtained from ChEMBL and employed in quantitative structure-activity relationship (QSAR) study so as to gain insights on their origin of bioactivity. AChE inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 different data splits using random forest. Generated models afforded R (2), [Formula: see text] and [Formula: see text] values in ranges of 0.66-0.93, 0.55-0.79 and 0.56-0.81 for the training set, 10-fold cross-validated set and external set, respectively. The best model built using the substructure count was selected according to the OECD guidelines and it afforded R (2), [Formula: see text] and [Formula: see text] values of 0.92 ± 0.01, 0.78 ± 0.06 and 0.78 ± 0.05, respectively. Furthermore, Y-scrambling was applied to evaluate the possibility of chance correlation of the predictive model. Subsequently, a thorough analysis of the substructure fingerprint count was conducted to provide informative insights on the inhibitory activity of AChE inhibitors. Moreover, Kennard-Stone sampling of the actives were applied to select 30 diverse compounds for further molecular docking studies in order to gain structural insights on the origin of AChE inhibition. Site-moiety mapping of compounds from the diversity set revealed three binding anchors encompassing both hydrogen bonding and van der Waals interaction. Molecular docking revealed that compounds 13, 5 and 28 exhibited the lowest binding energies of -12.2, -12.0 and -12.0 kcal/mol, respectively, against human AChE, which is modulated by hydrogen bonding, π-π stacking and hydrophobic interaction inside the binding pocket. These information may be used as guidelines for the design of novel and robust AChE inhibitors.

Aromatase, the rate-limiting enzyme that catalyzes the conversion of androgen to estrogen, plays an essential role in the development of estrogen-dependent breast cancer. Side effects due to aromatase inhibitors (AIs) necessitate the pursuit of novel inhibitor candidates with high selectivity, lower toxicity and increased potency. Designing a novel therapeutic agent against aromatase could be achieved computationally by means of ligand-based and structure-based methods. For over a decade, we have utilized both approaches to design potential AIs for which quantitative structure-activity relationships and molecular docking were used to explore inhibitory mechanisms of AIs towards aromatase. However, such approaches do not consider the effects that aromatase variants have on different AIs. In this study, proteochemometrics modeling was applied to analyze the interaction space between AIs and aromatase variants as a function of their substructural and amino acid features. Good predictive performance was achieved, as rigorously verified by 10-fold cross-validation, external validation, leave-one-compound-out cross-validation, leave-one-protein-out cross-validation and Y-scrambling tests. The investigations presented herein provide important insights into the mechanisms of aromatase inhibitory activity that could aid in the design of novel potent AIs as breast cancer therapeutic agents.

The aim was to analyze arc therapy techniques according to the number and position of the brain lesions reported by comparing dynamic noncoplanar conformal arcs (DCA), two coplanar full arcs (RAC) with volumetric-modulated arc therapy (VMAT), multiple noncoplanar partial arcs with VMAT (RANC), and two full arcs with VMAT and 10° table rotation (RAT). Patients with a single lesion (n= 10), multiple lesions (n = 10) or a single lesion close to organs at risk (n = 5) and previously treated with DCA were selected. For each patient, the DCA treatment was replanned with all VMAT techniques. All DCA plans were compared with VMAT plans and evaluated in regard to the different quality indices and dosimetric parameters. For single lesion, homogeneity index (HI) better results were found for the RANC technique (0.17 ± 0.05) compared with DCA procedure (0.27± 0.05). Concerning conformity index (CI), the RAT technique gave higher and better values (0.85 ± 0.04) compared with those obtained with the DCA technique (0.77 ± 0.05). DCA improved healthy brain protection (8.35 ± 5.61 cc vs. 10.52 ± 6.40 cc for RANC) and reduced monitor unit numbers (3046 ± 374 MU vs. 4651 ± 736 for RANC), even if global room occupation was higher. For multiple lesions, VMAT techniques provided better HI (0.16) than DCA (0.24 ± 0.07). The CI was improved with RAT (0.8 ± 0.08 for RAT vs. 0.71 ± 0.08 for DCA). The V10Gy healthy brain was better protected with DCA (9.27 ± 4.57 cc). Regarding the MU numbers: RANC < RAT< RAC < DCA. For a single lesion close to OAR, RAT achieved high degrees of homogeneity (0.27 ± 0.03 vs. 0.53 ± 0.2 for DCA) and conformity (0.72± 0.06vs. 0.56 ± 0.13 for DCA) while sparing organs at risk (Dmax = 12.36 ± 1.05Gyvs. 14.12 ± 0.59 Gy for DCA, and Dmean = 3.96 ± 3.57Gyvs. 4.72 ± 3.28Gy for DCA). On the other hand, MU numbers were lower with DCA (2254 ± 190 MUvs. 3438 ± 457 MU for RANC) even if overall time was inferior with RAC. For a single lesion, DCA provide better plan considering low doses to healthy brain even if quality indexes are better for the others techniques. For multiple lesions, RANC seems to be the best compromise, due to the ability to deliver a good conformity and homogeneity plan while sparing healthy brain tissue. For a single lesion close to organs at risk, RAT is the most appropriate technique.

Background: Tuberculous meningitis (TBM) caused by Mycobacterium tuberculosis resistant to antituberculosis drugs is an increasingly common clinical problem. This study aimed to evaluate drug resistance profiles of TBM isolates in adult patients in nine European countries involving 32 centers to provide insight into the empiric treatment of TBM., Methods: Mycobacterium tuberculosis was cultured from the cerebrospinal fluid (CSF) of 142 patients and was tested for susceptibility to first-line antituberculosis drugs, streptomycin (SM), isoniazid (INH), rifampicin (RIF) and ethambutol (EMB)., Results: Twenty of 142 isolates (14.1 %) were resistant to at least one antituberculosis drug, and five (3.5 %) were resistant to at least INH and RIF, [multidrug resistant (MDR)]. The resistance rate was 12, 4.9, 4.2 and 3.5 % for INH, SM, EMB and RIF, respectively. The monoresistance rate was 6.3, 1.4 and 0.7 % for INH, SM and EMB respectively. There was no monoresistance to RIF. The mortality rate was 23.8 % in fully susceptible cases while it was 33.3 % for those exhibiting monoresistance to INH, and 40 % in cases with MDR-TBM. In compared to patients without resistance to any first-line drug, the relative risk of death for INH-monoresistance and MDR-TBM was 1.60 (95 % CI, 0.38-6.82) and 2.14 (95 % CI, 0:34-13:42), respectively., Conclusion: INH-resistance and MDR rates seemed not to be worrisome in our study. However, considering their adverse effects on treatment, rapid detection of resistance to at least INH and RIF would be most beneficial for designing anti-TB therapy. Still, empiric TBM treatment should be started immediately without waiting the drug susceptibility testing.

Background: The Thwaites and Lancet scoring systems have been used in the rapid diagnosis of tuberculous meningitis (TBM). However, brucellar meningoencephalitis (BME) has similar characteristics with TBM. The ultimate aim of this study is to infer data to see if BME should be included in the differential diagnosis of TBM when these two systems suggest the presence of TBM., Method: BME and TBM patients from 35 tertiary hospitals were included in this study. Overall 294 adult patients with BME and 190 patients with TBM were enrolled. All patients involved in the study had microbiological confirmation for either TBM or BME. Finally, the Thwaites and Lancet scoring systems were assessed in both groups., Results: The Thwaites scoring system more frequently predicted BME cases (n = 292, 99.3%) compared to the TBM group (n = 182, 95.8%) (P = 0.017). According to the Lancet scoring system, the mean scores for BME and TBM were 9.43 ± 1.71 and 11.45 ± 3.01, respectively (P < 0.001). In addition, TBM cases were classified into "probable" category more significantly compared to BME cases, and BME cases were categorized into the "possible" category more frequently., Conclusions: When the Thwaites or Lancet scoring systems indicate TBM, brucellar etiology should also be taken into consideration particularly in endemic countries., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Unlabelled: In biology and chemistry, a key goal is to discover novel compounds affording potent biological activity or chemical properties. This could be achieved through a chemical intuition-driven trial-and-error process or via data-driven predictive modeling. The latter is based on the concept of quantitative structure-activity/property relationship (QSAR/QSPR) when applied in modeling the biological activity and chemical properties, respectively, of compounds. Data mining is a powerful technology underlying QSAR/QSPR as it harnesses knowledge from large volumes of high-dimensional data via multivariate analysis. Although extremely useful, the technicalities of data mining may overwhelm potential users, especially those in the life sciences. Herein, we aim to lower the barriers to access and utilization of data mining software for QSAR/QSPR studies. AutoWeka is an automated data mining software tool that is powered by the widely used machine learning package Weka. The software provides a user-friendly graphical interface along with an automated parameter search capability. It employs two robust and popular machine learning methods: artificial neural networks and support vector machines. This chapter describes the practical usage of AutoWeka and relevant tools in the development of predictive QSAR/QSPR models., Availability: The software is freely available at http://www.mt.mahidol.ac.th/autoweka.

Natural products have been an integral part of sustaining civilizations because of their medicinal properties. Past discoveries of bioactive natural products have relied on serendipity, and these compounds serve as inspiration for the generation of analogs with desired physicochemical properties. Bioactive natural products with therapeutic potential are abundantly available in nature and some of them are beyond exploration by conventional methods. The effectiveness of computational approaches as versatile tools for facilitating drug discovery and development has been recognized for decades, without exception, in the case of natural products. In the post-genomic era, scientists are bombarded with data produced by advanced technologies. Thus, rendering these data into knowledge that is interpretable and meaningful becomes an essential issue. In this regard, computational approaches utilize the existing data to generate knowledge that provides valuable understanding for addressing current problems and guiding the further research and development of new natural-derived drugs. Furthermore, several medicinal plants have been continuously used in many traditional medicine systems since antiquity throughout the world, and their mechanisms have not yet been elucidated. Therefore, the utilization of computational approaches and advanced synthetic techniques would yield great benefit to improving the world's health population and well-being.

Predicting unfavorable outcome is of paramount importance in clinical decision making. Accordingly, we designed this multinational study, which provided the largest case series of tuberculous meningitis (TBM). 43 centers from 14 countries (Albania, Croatia, Denmark, Egypt, France, Hungary, Iraq, Italy, Macedonia, Romania, Serbia, Slovenia, Syria, Turkey) submitted data of microbiologically confirmed TBM patients hospitalized between 2000 and 2012. Unfavorable outcome was defined as survival with significant sequela or death. In developing our index, binary logistic regression models were constructed via 200 replicates of database by bootstrap resampling methodology. The final model was built according to the selection frequencies of variables. The severity scale included variables with arbitrary scores proportional to predictive powers of terms in the final model. The final model was internally validated by bootstrap resampling. A total of 507 patients' data were submitted among which 165 had unfavorable outcome. Eighty-six patients died while 119 had different neurological sequelae in 79 (16%) patients. The full model included 13 variables. Age, nausea, vomiting, altered consciousness, hydrocephalus, vasculitis, immunosuppression, diabetes mellitus and neurological deficit remained in the final model. Scores 1-3 were assigned to the variables in the severity scale, which included scores of 1-6. The distribution of mortality for the scores 1-6 was 3.4, 8.2, 20.6, 31, 30 and 40.1%, respectively. Altered consciousness, diabetes mellitus, immunosuppression, neurological deficits, hydrocephalus, and vasculitis predicted the unfavorable outcome in the scoring and the cumulative score provided a linear estimation of prognosis.

Green fluorescent protein (GFP) has immense utility in biomedical imaging owing to its autofluorescent nature. In efforts to broaden the spectral diversity of GFP, there have been several reports of engineered mutants via rational design and random mutagenesis. Understanding the origins of spectral properties of GFP could be achieved by means of investigating its structure-activity relationship. The first quantitative structure-property relationship study for modeling the spectral properties, particularly the excitation and emission maximas, of GFP was previously proposed by us some years ago in which quantum chemical descriptors were used for model development. However, such simplified model does not consider possible effects that neighboring amino acids have on the conjugated π-system of GFP chromophore. This study describes the development of a unified proteochemometric model in which the GFP chromophore and amino acids in its vicinity are both considered in the same model. The predictive performance of the model was verified by internal and external validation as well as Y-scrambling. Our strategy provides a general solution for elucidating the contribution that specific ligand and protein descriptors have on the investigated spectral property, which may be useful in engineering novel GFP variants with desired characteristics., (Copyright © 2014 Wiley Periodicals, Inc.)