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Cell-free networks are expected to be a forthcoming (r)evolutionary step in the coming generation of mobile networks, the so called 6 G. While mobile infrastructure is often assumed to be deployed outdoor by the operators, reality is that most of the traffic has at least one of the communication ends located indoors. This paper introduces the problem of providing wireless service to a heterogeneous population made of indoor and outdoor users using an outdoor cell-free massive MIMO (CF-mMIMO) infrastructure. It is shown how the pervasive max-min criterion (in cell-free setups) that results in near-uniform quality-of-service to all users may lead to catastrophic consequences when some of the users happen to be indoor. This problem is analyzed in both communication directions, uplink and downlink, exposing the similarities and differences of these two scenarios. Direction-specific solutions are then provided that involve improving the channel estimation and connectivity of indoor users and modifying the power allocation so as to somehow compensate for the wall propagation indoor users have to endure. All the techniques introduced satisfy the scalability requirements thus making our proposal realistically implementable.

Sand media filters are especially recommended to prevent emitter clogging with loaded irrigation waters, but their performances rely on backwashing. Despite backwashing being a basic procedure needed to restore the initial filtration capacity, there is a lack of information about the solid removal efficiency along the media bed depth. An experimental filter with a 200 mm silica sand bed height was used to assess the effect of two operation velocities (30/45 and 60/75 (filtration/backwashing) m h−1) and two clogging particles (inorganic sand dust and organic from a reclaimed effluent) on the efficiency of backwashing for removing the total suspended solids retained in different media bed slices. The average solid removal backwashing efficiency was greater with organic particles (78%) than with inorganic ones (64%), reaching its maximum at a 5–15 mm bed depth. A higher operation velocity increased the solid removal efficiency by 16%, using organic particles, but no significant differences were observed with inorganic particles. The removal efficiencies across the media bed were more uniform with organic particles (63–89%) than with inorganic (40–85%), which makes it not advisable to reduce the media height when reclaimed effluents are used. This study may contribute to future improvements in sand media filter design and management.

Abstract Streamlined data-driven drug discovery remains challenging, especially in resource-limited settings. We present ZairaChem, an artificial intelligence (AI)- and machine learning (ML)-based tool for quantitative structure-activity/property relationship (QSAR/QSPR) modelling. ZairaChem is fully automated, requires low computational resources and works across a broad spectrum of datasets. We describe an end-to-end implementation at the H3D Centre, the leading integrated drug discovery unit in Africa, at which no prior AI/ML capabilities were available. By leveraging in-house data collected over a decade, we have developed a virtual screening cascade for malaria and tuberculosis drug discovery comprising 15 models for key decision-making assays ranging from whole-cell phenotypic screening and cytotoxicity to aqueous solubility, permeability, microsomal metabolic stability, cytochrome inhibition, and cardiotoxicity. We show how computational profiling of compounds, prior to synthesis and testing, can inform progression of frontrunner compounds at H3D. This project is a first-of-its-kind deployment at scale of AI/ML tools in a research centre operating in a low-resource setting.

Abstract Severe anemia is an important contributor to mortality in children with severe malaria. Anemia in malaria is a multi-factorial complication, since dyserythropoiesis, hemolysis and phagocytic clearance of uninfected red blood cells (RBCs) can contribute to this syndrome. High levels of oxidative stress and immune dysregulation have been proposed to contribute to severe malarial anemia, facilitating the clearance of uninfected RBCs. In a cohort of 552 Ugandan children with severe malaria, we measured the levels of xanthine oxidase (XO), an oxidative enzyme that is elevated in the plasma of malaria patients. The levels of XO in children with severe anemia were significantly higher compared to children with severe malaria not suffering from severe anemia. Levels of XO were inversely associated with RBC hemoglobin (ρ = − 0.25, p

Abstract We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15–17, 2022. Fifteen lectures were presented during a virtual public event with speakers from industry, academia, and non-for-profit organizations. Twelve hundred and ninety students and academics from more than 60 countries. During the meeting, applications, challenges, and opportunities in drug discovery, de novo drug design, ADME-Tox (absorption, distribution, metabolism, excretion and toxicity) property predictions, organic chemistry, peptides, and antibiotic resistance were discussed. The program along with the recordings of all sessions are freely available at https://www.difacquim.com/english/events/2022-colloquium/ .

Malaria can quickly progress from an uncomplicated infection into a life-threatening severe disease. However, the unspecificity of early symptoms often makes it difficult to identify patients at high risk of developing severe disease. Additionally, one of the most feared malaria complications — cerebral malaria — is challenging to diagnose, often resulting in treatment delays that can lead to adverse outcomes. To identify candidate biomarkers for the prognosis and/or diagnosis of severe and cerebral malaria, we have analyzed the transcriptomic response of human brain microvascular endothelial cells to erythrocytes infected with Plasmodium falciparum. Candidates were validated in plasma samples from a cohort of pediatric patients with malaria from Mozambique, resulting in the identification of several markers with capacity to distinguish uncomplicated from severe malaria, the most potent being the metallopeptidase ADAMTS18. Two other biomarkers, Angiopoietin-like-4 and Inhibin-βE were able to differentiate children with cerebral malaria within the severe malaria group, showing increased sensitivity after combination in a biomarker signature. The validation of the predicted candidate biomarkers in plasma of children with severe and cerebral malaria underscores the power of this transcriptomic approach and indicates that a specific endothelial response to P. falciparum–infected erythrocytes is linked to the pathophysiology of severe malaria.

Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge. Here, the authors present a resource that contains pre-calculated biomedical descriptors derived from a very large knowledge graph.

[This corrects the article DOI: 10.1371/journal.pgph.0000124.].

Abstract Background In spite of many years of research, our understanding of the molecular bases of Alzheimer’s disease (AD) is still incomplete, and the medical treatments available mainly target the disease symptoms and are hardly effective. Indeed, the modulation of a single target (e.g., β-secretase) has proven to be insufficient to significantly alter the physiopathology of the disease, and we should therefore move from gene-centric to systemic therapeutic strategies, where AD-related changes are modulated globally. Methods Here we present the complete characterization of three murine models of AD at different stages of the disease (i.e., onset, progression and advanced). We combined the cognitive assessment of these mice with histological analyses and full transcriptional and protein quantification profiling of the hippocampus. Additionally, we derived specific Aβ-related molecular AD signatures and looked for drugs able to globally revert them. Results We found that AD models show accelerated aging and that factors specifically associated with Aβ pathology are involved. We discovered a few proteins whose abundance increases with AD progression, while the corresponding transcript levels remain stable, and showed that at least two of them (i.e., lfit3 and Syt11) co-localize with Aβ plaques in the brain. Finally, we found two NSAIDs (dexketoprofen and etodolac) and two anti-hypertensives (penbutolol and bendroflumethiazide) that overturn the cognitive impairment in AD mice while reducing Aβ plaques in the hippocampus and partially restoring the physiological levels of AD signature genes to wild-type levels. Conclusions The characterization of three AD mouse models at different disease stages provides an unprecedented view of AD pathology and how this differs from physiological aging. Moreover, our computational strategy to chemically revert AD signatures has shown that NSAID and anti-hypertensive drugs may still have an opportunity as anti-AD agents, challenging previous reports.

Abstract We report the major conclusions of the online open-access workshop “Computational Applications in Secondary Metabolite Discovery (CAiSMD)” that took place from 08 to 10 March 2021. Invited speakers from academia and industry and about 200 registered participants from five continents (Africa, Asia, Europe, South America, and North America) took part in the workshop. The workshop highlighted the potential applications of computational methodologies in the search for secondary metabolites (SMs) or natural products (NPs) as potential drugs and drug leads. During 3 days, the participants of this online workshop received an overview of modern computer-based approaches for exploring NP discovery in the “omics” age. The invited experts gave keynote lectures, trained participants in hands-on sessions, and held round table discussions. This was followed by oral presentations with much interaction between the speakers and the audience. Selected applicants (early-career scientists) were offered the opportunity to give oral presentations (15 min) and present posters in the form of flash presentations (5 min) upon submission of an abstract. The final program available on the workshop website ( https://caismd.indiayouth.info/ ) comprised of 4 keynote lectures (KLs), 12 oral presentations (OPs), 2 round table discussions (RTDs), and 5 hands-on sessions (HSs). This meeting report also references internet resources for computational biology in the area of secondary metabolites that are of use outside of the workshop areas and will constitute a long-term valuable source for the community. The workshop concluded with an online survey form to be completed by speakers and participants for the goal of improving any subsequent editions.

Cytokines and chemokines are immune response molecules that display diverse functions, such as inflammation and immune regulation. In Plasmodium vivax infections, the uncontrolled production of these molecules is thought to contribute to pathogenesis and has been proposed as a possible predictor for disease complications. The objective of this study was to evaluate the cytokine profile of P. vivax malaria patients with different clinical outcomes to identify possible immune biomarkers for severe P. vivax malaria. The study included patients with non-severe (n = 56), or severe (n = 50) P. vivax malaria and healthy controls (n = 50). Patient plasma concentrations of IL-4, IL-2, CXCL10, IL-1β, TNF-α, CCL2, IL-17A, IL-6, IL-10, IFN-γ, IL-12p70, CXCL8 and active TGF-β1 were determined through flow cytometry. The levels of several cytokines and chemokines, CXCL10, IL-10, IL-6, IL-4, CCL2 and IFN-γ were found to be significantly higher in severe, compared to non-severe P. vivax malaria patients. Severe thrombocytopenia was positively correlated with IL-4, CXCL10, IL-6, IL-10 and IFN-γ levels, renal dysfunction was related to an increase in IL-2, IL-1β, IL-17A and IL-8, and hepatic impairment with CXCL10, MCP-1, IL-6 and IFN-γ. A Lasso regression model suggests that IL-4, IL-10, CCL2 and TGF-β might be developed as biomarkers for severity in P. vivax malaria. Severe P. vivax malaria patients present specific cytokine and chemokine profiles that are different from non-severe patients and that could potentially be developed as biomarkers for disease severity.

Small molecules bioactivity descriptors are enriched representations of compounds, reaching beyond chemical structures and capturing their known biological properties. Here the authors present a collection of deep neural networks able to infer bioactivity signatures for any compound of interest, even when little or no experimental information is available for them.

Background According to the Global Strategy to Accelerate the Elimination of Cervical Cancer launched by the World Health Organizations (WHO), 70% of all women should undergo cervical cancer (CC) screening by 2030. While integrated HIV care and CC screening services exist, lack of unified data and patient management systems hinder reaching the set WHO goals. We used record linkage methods to assess the number of women receiving HIV care and visual inspection with acetic acid (VIA) based CC screening at Kanyama First Level Hospital (FLH) in Lusaka, Zambia. Methods We retrieved HIV patient records from SmartCare, Zambia’s National Electronic Health Records system, and the CC screening database for the years 2004 to 2018, and 2010 to 2018, respectively. We restricted this pilot study to women attending Kanyama FLH for HIV services, and its surrounding clinics for CC screening. We used the following linkage variables: first name, last name, facility, and year of birth. We used a machine learning based approach with Random Forest classifiers to identify records from the HIV and CC screening services belonging to the same person. We created synthetic patient record datasets to train and test the linkages. After the linkage, we anonymized the data and retrieved screening status from the CC screening database. We assessed the percentage of women in HIV care undergoing CC screening and the respective screening results. We calculated the time to CC screening with reference to the start of the HIV Antiretroviral Therapy treatment (ART). Results We retrieved a total of 32,970 records from SmartCare and 176,736 records from CC screening databases. With the linkage we determined that of all women that ever accessed HIV treatment services at Kanyama FLH between 2004 and 2018, 2,999 (9%) received CC screening. Of those that were screened, 30% received the CC service at Kanyama FLH, while the rest from 13 surrounding facilities. 61% of women accessed CC services before starting ART. Of the women screened for CC, 371 had a positive VIA result (12%), 2,395 (80%) a negative and 233 (8%) had a missing or indeterminate result. When we restricted the analysis to women, who were eligible for screening (aged 25 to 59 years), the screening coverage was 9.6%. Conclusion Our study demonstrates the significance of record linkage efforts in determining CC screening coverage among women living with HIV. According to these findings, CC screening coverage among patients receiving ART services between 2004 and 2018 was at 9%, showing the depth of effort required to meet WHO targets at the time. The linkage output was primarily influenced by the scarcity of available linkage variables and data missingness, resulting in potentially overestimating or underestimating the CC coverage. This is typical of data systems in low- and middle-income countries; however, this study demonstrates that record linkage is useful in bridging these widespread data gaps.

Abstract Identification of actionable genomic vulnerabilities is key to precision oncology. Utilizing a large-scale drug screening in patient-derived xenografts, we uncover driver gene alteration connections, derive driver co-occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug-response predictors attains an average balanced accuracy of 58% in a cross-validation setting, rising to 66% for a subset of high-confidence predictions. We experimentally validated 12 out of 14 predictions in mice and adapted our strategy to obtain drug-response models from patients’ progression-free survival data. Our strategy reveals links between oncogenic alterations, increasing the clinical impact of genomic profiling.

As the response to the HIV epidemic in sub-Saharan Africa continues to mature, a growing number of people living with HIV (PLHIV) are aging and risk for non-communicable diseases increases. Routine laboratory tests of serum creatinine have been conducted to assess HIV treatment (ART) suitability. Here we utilize those measures to assess kidney function impairment among those initiating ART. Identification of non-communicable disease (NCD) risks among those in HIV care creates opportunity to improve public health through care referral and/or NCD/HIV care integration. We estimated glomerular filtration rates (eGFR) using routinely collected serum creatinine measures among a cohort of PLHIV with an HIV care visit at one of 113 Centre for Infectious Disease Research Zambia (CIDRZ) supported sites between January 1, 2011 and December 31, 2017, across seven of the ten provinces in Zambia. We used mixed-effect Poisson regression to assess predictors of eGFR

Pressurized sand media filters are commonly used in drip irrigation systems to prevent emitter clogging. However, the performance of these filters may be improved with more information about the retention of solids at different bed depths under different filter operation conditions and irrigation water sources. In this study, experiments in a scaled sand media filter were conducted to clog the filter with two different filtration velocities (30 and 60 m h−1) and two-particle types (inorganic from A4 coarse sand dust and organic from a reclaimed effluent). The suspended solids retained in slices of 5 mm (in the first 20 mm of the bed) and 20 mm (from 20 to 200 mm depth) thick were determined following the van Staden and Haarhoff (2011) procedure. The solids retained in each slice per mass of media were significantly (p < 0.05) affected by the interaction between the filtration velocity, the bed depth, and the particle type. The solids retained in the first 5 mm of the bed were significantly higher than at other depths. Moreover, inorganic solids were retained more in upper slices than organic ones. Therefore, media depths may be adjusted depending on the irrigation water source to optimize media use.

Abstract Background The integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for previous knowledge about drug action. In particular, transcriptional signatures of drug sensitivity may guide drug repositioning, prioritize drug combinations, and point to new therapeutic biomarkers. However, the inherent complexity of transcriptional signatures, with thousands of differentially expressed genes, makes them hard to interpret, thus giving poor mechanistic insights and hampering translation to clinics. Methods To simplify drug signatures, we have developed a network-based methodology to identify functionally coherent gene modules. Our strategy starts with the calculation of drug-gene correlations and is followed by a pathway-oriented filtering and a network-diffusion analysis across the interactome. Results We apply our approach to 189 drugs tested in 671 cancer cell lines and observe a connection between gene expression levels of the modules and mechanisms of action of the drugs. Further, we characterize multiple aspects of the modules, including their functional categories, tissue-specificity, and prevalence in clinics. Finally, we prove the predictive capability of the modules and demonstrate how they can be used as gene sets in conventional enrichment analyses. Conclusions Network biology strategies like module detection are able to digest the outcome of large-scale pharmacogenomic initiatives, thereby contributing to their interpretability and improving the characterization of the drugs screened.

Summary: Background: Globally, cervical cancer is the fourth leading cause of cancer-related death among women. Poor uptake of screening services contributes to the high mortality. We aimed to examine screening frequency, predictors of screening results, and patterns of sensitisation strategies by age group in a large, programmatic cohort. Methods: We did a cohort study including 11 government health facilities in Lusaka, Zambia, in which we reviewed routine programmatic data collected through the Cervical Cancer Prevention Program in Zambia (CCPPZ). Participants who underwent cervical cancer screening in one of the participating study sites were considered for study inclusion if they had a screening result. Follow-up was accomplished per national guidelines. We did descriptive analyses and mixed-effects logistic regression for cervical cancer screening results allowing random effects at the individual and clinic level. Findings: Between Jan 1, 2010, and July 31, 2019, we included 183 165 women with 204 225 results for visual inspection with acetic acid and digital cervicography (VIAC) in the analysis. Of all those screened, 21 326 (10·4%) were VIAC-positive, of whom 16 244 (76·2%) received treatment. Of 204 225 screenings, 92 838 (45·5%) were in women who were HIV-negative, 76 607 (37·5%) were in women who were HIV-positive, and 34 780 (17·0%) had an unknown HIV status. Screening frequency increased 65·7% between 2010 and 2019 with most appointments being first-time screenings (n=158 940 [77·8%]). Women with HIV were more likely to test VIAC-positive than women who were HIV-negative (adjusted odds ratio 3·60, 95% CI 2·14–6·08). Younger women (≤29 years) with HIV had the highest predictive probability (18·6%, 95% CI 14·2–22·9) of screening positive. Interpretation: CCPPZ has effectively increased women's engagement in screening since its inception in 2006. Customised sensitisation strategies relevant to different age groups could increase uptake and adherence to screening. The high proportion of screen positivity in women younger than 20 years with HIV requires further consideration. Our data are not able to discern if women with HIV have earlier disease onset or whether this difference reflects misclassification of disease in an age group with a higher sexually transmitted infection prevalence. These data inform scale-up efforts required to achieve WHO elimination targets. Funding: US President's Emergency Plan for AIDS Relief.

Irrigation is very important for global food production and, therefore, it is key to moving towards increasingly sustainable irrigation systems. However, the adoption of more efficient water-use techniques, such as drip irrigation, increases energy consumption. A large part of the efficiency of drip irrigation systems depends on the equipment used, such as filters. The environmental impact of three media filters (a prototype with a porous media underdrain and two commercial filters, with inserted domes and collector arms underdrains, respectively) with different bed heights, filtration rates, and medium materials was studied using the life cycle analysis (LCA). Under the operating conditions that minimize the impact, the inserted domes design has the lowest overall impact, achieving reductions of up to 432% and 18% in some impacts regarding the porous media and the collector arm, respectively. A porous media filter has a better energy performance, but its prototype status hinders it in the raw material, building, and end-of-life phases. Conversely, the arm collector has the worst environmental performance. This work shows that filter designs should prioritize solutions that allow for minimizing the energy consumption during their operation to increase its sustainability.

Abstract Background The widespread incorporation of next-generation sequencing into clinical oncology has yielded an unprecedented amount of molecular data from thousands of patients. A main current challenge is to find out reliable ways to extrapolate results from one group of patients to another and to bring rationale to individual cases in the light of what is known from the cohorts. Results We present OncoGenomic Landscapes, a framework to analyze and display thousands of cancer genomic profiles in a 2D space. Our tool allows users to rapidly assess the heterogeneity of large cohorts, enabling the comparison to other groups of patients, and using driver genes as landmarks to aid in the interpretation of the landscapes. In our web-server, we also offer the possibility of mapping new samples and cohorts onto 22 predefined landscapes related to cancer cell line panels, organoids, patient-derived xenografts, and clinical tumor samples. Conclusions Contextualizing individual subjects in a more general landscape of human cancer is a valuable aid for basic researchers and clinical oncologists trying to identify treatment opportunities, maybe yet unapproved, for patients that ran out of standard therapeutic options. The web-server can be accessed at https://oglandscapes.irbbarcelona.org/.

Tumors often exhibit a pH gradient, with an acidic extracellular space and alkaline cytoplasm. Here the authors develop a computational model to show how alkaline pHi supports changes inherent to cancer cell metabolism and acidification disables these adaptations, and demonstrate the effect of acidic pHi on breast cancer cell survival.

It is well-known that kinked phenacenes are more stable than their isomeric linear acenes, the archetypal example being phenanthrene that is more stable than anthracene by about 4–8 kcal/mol. In previous studies, the origin of the higher stability of kinked polycyclic aromatic hydrocarbons (PAHs) was found to be better π-bonding interactions, i.e., larger aromaticity, in kinked as compared to linear PAHs. Some years ago, however, Dominikowska and Palusiak (2011) found that dicationic linear anthracene is more stable than the dicationic kinked phenanthrene. Therefore, these authors showed that, in some cases, the linear topology in PAHs can be preferred over the kinked one. Our results using energy decomposition analyses in combination with the turn-upside-down approach show that the origin of the higher stability of dicationic anthracene is the same as in the neutral species, i.e., better π-bonding interactions. A similar result is found for the kinked and straight pyrano-chromenes. We conclude that the aromaticity is the driving force that determines the relative stability of kinked vs. straight topologies in PAHs.

Insertion of reporter cassettes into the Lgr5 locus has enabled the characterization of mouse intestinal stem cells (ISCs). However, low cell surface abundance of LGR5 protein and lack of high-affinity anti-LGR5 antibodies represent a roadblock to efficiently isolate human colonic stem cells (hCoSCs). We set out to identify stem cell markers that would allow for purification of hCoSCs. In an unbiased approach, membrane-enriched protein fractions derived from in vitro human colonic organoids were analyzed by quantitative mass spectrometry. Protein tyrosine pseudokinase PTK7 specified a cell population within human colonic organoids characterized by highest self-renewal and re-seeding capacity. Antibodies recognizing the extracellular domain of PTK7 allowed us to isolate and expand hCoSCs directly from patient-derived mucosa samples. Human PTK7+ cells display features of canonical Lgr5+ ISCs and include a fraction of cells that undergo differentiation toward enteroendocrine lineage that resemble crypt label retaining cells (LRCs).

In the era of systems biology, multi-target pharmacological strategies hold promise for tackling disease-related networks. In this regard, drug promiscuity may be leveraged to interfere with multiple receptors: the so-called polypharmacology of drugs can be anticipated by analyzing the similarity of binding sites across the proteome. Here, we perform a pairwise comparison of 90,000 putative binding pockets detected in 3,700 proteins, and find that 23,000 pairs of proteins have at least one similar cavity that could, in principle, accommodate similar ligands. By inspecting these pairs, we demonstrate how the detection of similar binding sites expands the space of opportunities for the rational design of drug polypharmacology. Finally, we illustrate how to leverage these opportunities in protein-protein interaction networks related to several therapeutic classes and tumor types, and in a genome-scale metabolic model of leukemia.

Recent insights suggest that non-specific and/or promiscuous enzymes are common and active across life. Understanding the role of such enzymes is an important open question in biology. Here we develop a genome-wide method, PROPER, that uses a permissive PSI-BLAST approach to predict promiscuous activities of metabolic genes. Enzyme promiscuity is typically studied experimentally using multicopy suppression, in which over-expression of a promiscuous 'replacer' gene rescues lethality caused by inactivation of a 'target' gene. We use PROPER to predict multicopy suppression in Escherichia coli, achieving highly significant overlap with published cases (hypergeometric p = 4.4e-13). We then validate three novel predicted target-replacer gene pairs in new multicopy suppression experiments. We next go beyond PROPER and develop a network-based approach, GEM-PROPER, that integrates PROPER with genome-scale metabolic modeling to predict promiscuous replacements via alternative metabolic pathways. GEM-PROPER predicts a new indirect replacer (thiG) for an essential enzyme (pdxB) in production of pyridoxal 5'-phosphate (the active form of Vitamin B6), which we validate experimentally via multicopy suppression. We perform a structural analysis of thiG to determine its potential promiscuous active site, which we validate experimentally by inactivating the pertaining residues and showing a loss of replacer activity. Thus, this study is a successful example where a computational investigation leads to a network-based identification of an indirect promiscuous replacement of a key metabolic enzyme, which would have been extremely difficult to identify directly.