Heavy Metal Bioaccumulation in Peruvian Food and Medicinal Products.

To better query regional sources of metal(loid) exposure in an under-communicated region, available scientific literature from 50 national universities (undergraduate and graduate theses and dissertations), peer-reviewed journals, and reports published in Spanish and English were synthesized with a focus on metal(loid) bioaccumulation in Peruvian food and medicinal products utilized locally. The study considered 16 metal(loid)s that are known to exert toxic impacts on humans (Hg, Al, Sb, As, Ba, Be, Cd, Cr, Sn, Ni, Ag, Pb, Se, Tl, Ti, and U). A total of 1907 individual analyses contained within 231 scientific publications largely conducted by Peruvian universities were analyzed. These analyses encompassed 239 reported species classified into five main food/medicinal groups-plants, fish, macroinvertebrates and mollusks, mammals, and "others" category. Our benchmark for comparison was the World Health Organization (Codex Alimentarius) standards. The organisms most frequently investigated included plants such as asparagus, corn, cacao, and rice; fish varieties like trout, tuna, and catfish; macroinvertebrates and mollusks including crab and shrimp; mammals such as alpaca, cow, chicken eggs, and milk; and other categories represented by propolis, honey, lichen, and edible frog. Bioaccumulation-related research increased from 2 to more than 25 publications per year between 2006 and 2022. The results indicate that Peruvian food and natural medicinal products can have dangerous levels of metal(loid)s, which can cause health problems for consumers. Many common and uncommon food/medicinal products and harmful metals identified in this analysis are not regulated on the WHO's advisory lists, suggesting the urgent need for stronger regulations to ensure public safety. In general, Cd and Pb are the metals that violated WHO standards the most, although commonly non-WHO regulated metals such as Hg, Al, As, Cr, and Ni are also a concern. Metal concentrations found in Peru are on many occasions much higher than what has been reported elsewhere. We conclude that determining the safety of food/medicinal products is challenging due to varying metal concentrations that are influenced not only by metal type but also geographical location. Given the scarcity of research findings in many regions of Peru, urgent attention is required to address this critical knowledge gap and implement effective regulatory measures to protect public health.

Photosynthetic pretreatment increases membrane-based rejection of boron and arsenic.

The metalloids boron and arsenic are ubiquitous and difficult to remove during water treatment. As chemical pretreatment using strong base and oxidants can increase their rejection during membrane-based nanofiltration (NF), we examined a nature-based pretreatment approach using benthic photosynthetic processes inherent in a unique type of constructed wetland to assess whether analogous gains can be achieved without the need for exogenous chemical dosing. During peak photosynthesis, the pH of the overlying clear water column above a photosynthetic microbial mat (biomat) that naturally colonizes shallow, open water constructed wetlands climbs from circumneutral to approximately 10. This biological increase in pH was reproduced in a laboratory bioreactor and resulted in analogous increases in NF rejection of boron and arsenic that is comparable to chemical dosing. Rejection across the studied pH range was captured using a monoprotic speciation model. In addition to this mechanism, the biomat accelerated the oxidation of introduced arsenite through a combination of abiotic and biotic reactions. This resulted in increases in introduced arsenite rejection that eclipsed those achieved solely by pH. Capital, operation, and maintenance costs were used to benchmark the integration of this constructed wetland against chemical dosing for water pretreatment, manifesting long-term (sub-decadal) economic benefits for the wetland-based strategy in addition to social and environmental benefits. These results suggest that the integration of nature-based pretreatment approaches can increase the sustainability of membrane-based and potentially other engineered treatment approaches for challenging water contaminants.

Species distribution and antifungal susceptibility profiles of clinical and environmental Fusarium isolates from Mexico: A multicenter study.

Fusarium spp. has emerged as an opportunistic etiological agent with clinical manifestations varying from localized infections to deep-seated systemic disease. It is also a phytopathogen of economic impact. There are few reports on the species diversity of this genus, and no comprehensive studies on the epidemiology nor the antifungal susceptibility of Fusarium in Mexico. The present multicentric study aims to shed light on the species distribution and antifungal susceptibility patterns of 116 strains of Fusarium isolated from clinical and environmental samples. Isolates were identified by standard phenotypic characteristics and by sequencing of the ITS (internal transcribed spacer), TEF1 (translation elongation factor 1-α), RPB2 (RNA polymerase II core subunit), and/or CAM1 (calmodulin) regions. Susceptibility tests were carried out against 15 antifungals of clinical and agricultural use. Regarding Fusarium distribution, we identified 27 species belonging to eight different species complexes. The most frequently isolated species for both clinical and environmental samples were F. falciforme (34%), F. oxysporum sensu stricto (12%), F. keratoplasticum (8%), and F. solani sensu stricto (8%). All Fusarium isolates showed minimum inhibitory concentrations (MICs) equal to or above the maximum concentration evaluated for fluconazole, 5-fluocytosine, caspofungin, micafungin, and anidulafungin. All isolates had a MIC of ≤16 µg/mL for voriconazole, with a mode of 4 µg/mL. F. verticillioides appeared to be the most susceptible to all antifungals tested.

Changes in the Proliferation of the Neural Progenitor Cells of Adult Mice Chronically Infected with Toxoplasma gondii.

During Toxoplasma gondii chronic infection, certain internal factors that trigger the proliferation of neural progenitor cells (NPCs), such as brain inflammation, cell death, and changes in cytokine levels, are observed. NPCs give rise to neuronal cell types in the adult brain of some mammals. NPCs are capable of dividing and differentiating into a restricted repertoire of neuronal and glial cell types. In this study, the proliferation of NPCs was evaluated in CD-1 adult male mice chronically infected with the T. gondii ME49 strain. Histological brain sections from the infected mice were evaluated in order to observe T. gondii tissue cysts. Sagittal and coronal sections from the subventricular zone of the lateral ventricles and from the subgranular zone of the hippocampal dentate gyrus, as well as sagittal sections from the rostral migratory stream, were obtained from infected and non-infected mice previously injected with bromodeoxyuridine (BrdU). A flotation immunofluorescence technique was used to identify BrdU+ NPC. The scanning of BrdU+ cells was conducted using a confocal microscope, and the counting was performed with ImageJ® software (version 1.48q). In all the evaluated zones from the infected mice, a significant proliferation of the NPCs was observed when compared with that of the control group. We concluded that chronic infection with T. gondii increased the proliferation of NPCs in the three evaluated zones. Regardless of the role these cells are playing, our results could be useful to better understand the pathogenesis of chronic toxoplasmosis.

Development of a Low-Cost Artificial Vision System as an Alternative for the Automatic Classification of Persian Lemon: Prototype Test Simulation.

In the present research work, an algorithm of artificial neural network (ANN) has been developed based on the processing of digital images of Persian lemons with the aim of optimizing the quality control of the product. For this purpose, the physical properties (weight, thickness of the peel, diameter, length, and color) of 90 lemons selected from the company Esperanza de San José Ornelas SPR de RL (Jalisco, Mexico) were studied, which were divided into three groups (Category "extra", Category I, and Category II) according to their characteristics. The parameters of weight (26.50 ± 3.00 g), diameter/length (0.92 ± 0.08) and thickness of the peel (1.50 ± 0.29 mm) did not present significant differences between groups. On the other hand, the color (determined by the RGB and HSV models) presents statistically significant changes between groups. Due to the above, the proposed ANN correctly classifies 96.60% of the data obtained for each of the groups studied. Once the ANN was trained, its application was tested in an automatic classification process. For this purpose, a prototype based on the operation of a stepper motor was simulated using Simulink from Matlab, which is connected to three ideal switches powered by three variable pulse generators that receive the information from an ANN and provide the corresponding signal for the motor to turn to a specific position. Manual classification is a process that requires expert personnel and is prone to human error. The scientific development presented shows an alternative for the automation of the process using low-cost computational tools as a potential alternative.

Inhibition of Pertussis Toxin by Human α-Defensins-1 and -5: Differential Mechanisms of Action.

Whooping cough is a severe childhood disease, caused by the bacterium Bordetella pertussis, which releases pertussis toxin (PT) as a major virulence factor. Previously, we identified the human antimicrobial peptides α-defensin-1 and -5 as inhibitors of PT and demonstrated their capacity to inhibit the activity of the PT enzyme subunit PTS1. Here, the underlying mechanism of toxin inhibition was investigated in more detail, which is essential for developing the therapeutic potential of these peptides. Flow cytometry and immunocytochemistry revealed that α-defensin-5 strongly reduced PT binding to, and uptake into cells, whereas α-defensin-1 caused only a mild reduction. Conversely, α-defensin-1, but not α-defensin-5 was taken up into different cell lines and interacted with PTS1 inside cells, based on proximity ligation assay. In-silico modeling revealed specific interaction interfaces for α-defensin-1 with PTS1 and vice versa, unlike α-defensin-5. Dot blot experiments showed that α-defensin-1 binds to PTS1 and even stronger to its substrate protein Gαi in vitro. NADase activity of PTS1 in vitro was not inhibited by α-defensin-1 in the absence of Gαi. Taken together, these results suggest that α-defensin-1 inhibits PT mainly by inhibiting enzyme activity of PTS1, whereas α-defensin-5 mainly inhibits cellular uptake of PT. These findings will pave the way for optimization of α-defensins as novel therapeutics against whooping cough.

Age-Related Differences in Ocular Features of a Naturalistic Free-Ranging Population of Rhesus Macaques.

Purpose: Rhesus macaques (Macaca mulatta) are the premier nonhuman primate model for studying human health and disease. We investigated if age was associated with clinically relevant ocular features in a large cohort of free-ranging rhesus macaques from Cayo Santiago, Puerto Rico. Methods: We evaluated 120 rhesus macaques (73 males, 47 females) from 0 to 29 years old (mean ± SD: 12.6 ± 6.4) from September to December 2021. The ophthalmic evaluation included intraocular pressure (IOP) assessment, corneal pachymetry, biomicroscopy, A-scan biometry, automated refraction, and fundus photography after pupil dilation. The associations of age with the outcomes were investigated through multilevel mixed-effects models adjusted for sex and weight. Results: On average, IOP, pachymetry, axial length, and automated refraction spherical equivalent were 18.37 ± 4.68 mmHg, 474.43 ± 32.21 µm, 19.49 ± 1.24 mm, and 0.30 ± 1.70 diopters (D), respectively. Age was significantly associated with pachymetry (ß coefficient = -1.20; 95% confidence interval [CI], -2.27 to -0.14; P = 0.026), axial length (ß coefficient = 0.03; 95% CI, 0.01 to 0.05; P = 0.002), and spherical equivalent (ß coefficient = -0.12; 95% CI, -0.22 to -0.02; P = 0.015). No association was detected between age and IOP. The prevalence of cataracts in either eye was 10.83% (95% CI, 6.34-17.89) and was significantly associated with age (odds ratio [OR] = 1.20; 95% CI, 1.06-1.36; P = 0.004). Retinal drusen in either eye was observed in 15.00% (95% CI, 9.60-22.68) of animals, which was also significantly associated with age (OR = 1.14; 95% CI, 1.02-1.27; P = 0.020). Conclusions: Rhesus macaques exhibit age-related ocular associations similar to those observed in human aging, including decreased corneal thickness, increased axial length, myopic shift, and higher prevalence of cataract and retinal drusen.

Cm-p5 Peptide Dimers Inhibit Biofilms of Candida albicans Clinical Isolates, C. parapsilosis and Fluconazole-Resistant Mutants of C. auris.

Antimicrobial peptides (AMPs) represent a promising class of therapeutic biomolecules that show antimicrobial activity against a broad range of microorganisms, including life-threatening pathogens. In contrast to classic AMPs with membrane-disrupting activities, new peptides with a specific anti-biofilm effect are gaining in importance since biofilms could be the most important way of life, especially for pathogens, as the interaction with host tissues is crucial for the full development of their virulence in the event of infection. Therefore, in a previous study, two synthetic dimeric derivatives (parallel Dimer 1 and antiparallel Dimer 2) of the AMP Cm-p5 showed specific inhibition of the formation of Candida auris biofilms. Here we show that these derivatives are also dose-dependently effective against de novo biofilms that are formed by the widespread pathogenic yeasts C. albicans and C. parapsilosis. Moreover, the activity of the peptides was demonstrated even against two fluconazole-resistant strains of C. auris.

Musa spp. cultivars as a neutralising source against some toxic activities of Bothrops and Crotalus genus snake venoms.

Accidents involving snakes from Bothrops spp. and Crotalus spp. constitute the most important cause of envenomation in Brazil and Argentina. Musa spp. (banana) have been reported to be used in popular medicine against snakebite by the members of the Canudos Settlement, located in Goiás. In this way, the aim of this work was to evaluate the antivenom effect of the Ouro (AA), Prata (AAB), Prata-anã (AAB) and Figo (ABB) cultivars against in vitro (phospholipase, coagulation and proteolytic) and in vivo (lethality and toxicity) activities caused by the venoms and toxicity (Artemia salina nauplii and Danio rerio embryos) of Musa spp. as well as the annotation of chemical compounds possibly related to these activities. From the in vitro antiophidic tests with the sap, we observed 100% inhibition of the phospholipase and coagulant activities with the cultivars Prata-anã and Figo against the venoms of B. alternatus and C. d. collineatus, B. diporus and B. pauloensis, respectively, and neutralisation of the lethality against the B. diporus venom. It was observed that the cultivars of Musa spp. did not show toxicity against Artemia salina nauplii and Danio rerio embryos. The sap analysis via HPLC-MS/MS allowed the annotation of the 13 compounds: abscisic acid, shikimic acid, citric acid, quinic acid, afzelechin, Glp-hexose, glucose, sucrose, isorhamnetin-3-O-galactoside-6-raminoside, kaempferol-3-glucoside-3-raminoside, myricetin-3-O-rutinoside, procyanidin B1 and rutin. Therefore, it can be seen that Musa spp. is a potential therapeutic agent that can act to neutralise the effects caused by snakebites.

Scalable and customizable parallel flow-through reactors to quantify biological processes related to contaminant attenuation by photosynthetic wetland microbial mats.

Shallow, unit process open water wetlands harbor a benthic microbial mat capable of removing nutrients, pathogens, and pharmaceuticals at rates that rival or exceed those of more traditional systems. A deeper understanding of the treatment capabilities of this non-vegetated, nature-based system is currently hampered by experimentation limited to demonstration-scale field systems and static lab-based microcosms that integrate field-derived materials. This limits fundamental mechanistic knowledge, extrapolation to contaminants and concentrations not present at current field sites, operational optimization, and integration into holistic water treatment trains. Hence, we have developed stable, scalable, and tunable laboratory reactor analogs that offer the capability to manipulate variables such as influent rates, aqueous geochemistry, light duration, and light intensity gradations within a controlled laboratory environment. The design is composed of an experimentally adaptable set of parallel flow-through reactors and controls that can contain field-harvested photosynthetic microbial mats ("biomat") and could be adapted for analogous photosynthetically active sediments or microbial mats. The reactor system is contained within a framed laboratory cart that integrates programable LED photosynthetic spectrum lights. Peristaltic pumps are used to introduce specified growth media, environmentally derived, or synthetic waters at a constant rate, while a gravity-fed drain on the opposite end allows steady-state or temporally variable effluent to be monitored, collected, and analyzed. The design allows for dynamic customization based on experimental needs without confounding environmental pressures and can be easily adapted to study analogous aquatic, photosynthetically driven systems, particularly where biological processes are contained within benthos. The diel cycles of pH and dissolved oxygen (DO) are used as geochemical benchmarks for the interplay of photosynthetic and heterotrophic respiration and likeness to field systems. Unlike static microcosms, this flow-through system remains viable (based on pH and DO fluctuations) and has at present been maintained for more than a year with original field-based materials.•Lab-scale flow-through reactors enable controlled and accessible exploration of shallow, open water constructed wetland function and applications.•The footprint and operating parameters minimize resources and hazardous waste while allowing for hypothesis-driven experiments.•A parallel negative control reactor quantifies and minimizes experimental artifacts.

Heavy metal removal by the photosynthetic microbial biomat found within shallow unit process open water constructed wetlands.

Nature-based solutions offer a sustainable alternative to labor and chemical intensive engineered treatment of metal-impaired waste streams. Shallow, unit process open water (UPOW) constructed wetlands represent a novel design where benthic photosynthetic microbial mats (biomat) coexist with sedimentary organic matter and inorganic (mineral) phases, creating an environment for multiple-phase interactions with soluble metals. To query the interplay of dissolved metals with inorganic and organic fractions, biomat was harvested from two distinct systems: the demonstration-scale UPOW within the Prado constructed wetlands complex ("Prado biomat", 88 % inorganic) and a smaller pilot-scale system ("Mines Park (MP) biomat", 48 % inorganic). Both biomats accumulated detectable background concentrations of metals of toxicological concern (Zn, Cu, Pb, and Ni) by assimilation from waters that did not exceed regulatory thresholds for these metals. Augmentation in laboratory microcosms with a mixture of these metals at ecotoxicologically relevant concentrations revealed a further capacity for metal removal (83-100 %). Experimental concentrations encapsulated the upper range of surface waters in the metal-impaired Tambo watershed in Peru, where a passive treatment technology such as this could be applied. Sequential extractions demonstrated that metal removal by mineral fractions is more important in Prado than MP biomat, possibly due to a higher proportion and mass of iron and other minerals from Prado-derived materials. Geochemical modeling using PHREEQC suggests that in addition to sorption/surface complexation of metals to mineral phases (modeled as iron (oxyhydr)oxides), diatom and bacterial functional groups (carboxyl, phosphoryl, and silanol) also play an important role in soluble metal removal. By comparing sequestered metal phases across these biomats with differing inorganic content, we propose that sorption/surface complexation and incorporation/assimilation of both inorganic and organic constituents of the biomat play a dominant role in metal removal potential by UPOW wetlands. This knowledge could be applied to passively treat metal impaired waters in analogous and remote regions.

A Current Review of Water Pollutants in American Continent: Trends and Perspectives in Detection, Health Risks, and Treatment Technologies.

Currently, water pollution represents a serious environmental threat, causing an impact not only to fauna and flora but also to human health. Among these pollutants, inorganic and organic pollutants are predominantly important representing high toxicity and persistence and being difficult to treat using current methodologies. For this reason, several research groups are searching for strategies to detect and remedy contaminated water bodies and effluents. Due to the above, a current review of the state of the situation has been carried out. The results obtained show that in the American continent a high diversity of contaminants is present in the water bodies affecting several aspects, in which in some cases, there exists alternatives to realize the remediation of contaminated water. It is concluded that the actual challenge is to establish sanitation measures at the local level based on the specific needs of the geographical area of interest. Therefore, water treatment plants must be designed according to the contaminants present in the water of the region and tailored to the needs of the population of interest.

Aptamers as Novel Binding Molecules on an Antimicrobial Peptide-Armored Composite Hydrogel Wound Dressing for Specific Removal and Efficient Eradication of Pseudomonas aeruginosa.

Here we present for the first time a potential wound dressing material implementing aptamers as binding entities to remove pathogenic cells from newly contaminated surfaces of wound matrix-mimicking collagen gels. The model pathogen in this study was the Gram-negative opportunistic bacterium Pseudomonas aeruginosa, which represents a considerable health threat in hospital environments as a cause of severe infections of burn or post-surgery wounds. A two-layered hydrogel composite material was constructed based on an established eight-membered focused anti-P. aeruginosa polyclonal aptamer library, which was chemically crosslinked to the material surface to form a trapping zone for efficient binding of the pathogen. A drug-loaded zone of the composite released the C14R antimicrobial peptide to deliver it directly to the bound pathogenic cells. We demonstrate that this material combining aptamer-mediated affinity and peptide-dependent pathogen eradication can quantitatively remove bacterial cells from the "wound" surface, and we show that the surface-trapped bacteria are completely killed. The drug delivery function of the composite thus represents an extra safeguarding property and thus probably one of the most important additional advances of a next-generation or smart wound dressing ensuring the complete removal and/or eradication of the pathogen of a freshly infected wound.

Identification and Characterization of Three New Antimicrobial Peptides from the Marine Mollusk Nerita versicolor (Gmelin, 1791).

Mollusks have been widely investigated for antimicrobial peptides because their humoral defense against pathogens is mainly based on these small biomolecules. In this report, we describe the identification of three novel antimicrobial peptides from the marine mollusk Nerita versicolor. A pool of N. versicolor peptides was analyzed with nanoLC-ESI-MS-MS technology, and three potential antimicrobial peptides (Nv-p1, Nv-p2 and Nv-p3) were identified with bioinformatical predictions and selected for chemical synthesis and evaluation of their biological activity. Database searches showed that two of them show partial identity to histone H4 peptide fragments from other invertebrate species. Structural predictions revealed that they all adopt a random coil structure even when placed near a lipid bilayer patch. Nv-p1, Nv-p2 and Nv-p3 exhibited activity against Pseudomonas aeruginosa. The most active peptide was Nv-p3 with an inhibitory activity starting at 1.5 µg/mL in the radial diffusion assays. The peptides were ineffective against Klebsiella pneumoniae, Listeria monocytogenes and Mycobacterium tuberculosis. On the other hand, these peptides demonstrated effective antibiofilm action against Candida albicans, Candida parapsilosis and Candida auris but not against the planktonic cells. None of the peptides had significant toxicity on primary human macrophages and fetal lung fibroblasts at effective antimicrobial concentrations. Our results indicate that N. versicolor-derived peptides represent new AMP sequences and have the potential to be optimized and developed into antibiotic alternatives against bacterial and fungal infections.

Graph neural networks and cross-protocol analysis for detecting malicious IP addresses.

An internet protocol (IP) address is the foundation of the Internet, allowing connectivity between people, servers, Internet of Things, and services across the globe. Knowing what is connecting to what and where connections are initiated is crucial to accurately assess a company's or individual's security posture. IP reputation assessment can be quite complex because of the numerous services that may be hosted on that IP address. For example, an IP might be serving millions of websites from millions of different companies like web hosting companies often do, or it could be a large email system sending and receiving emails for millions of independent entities. The heterogeneous nature of an IP address typically makes it challenging to interpret the security risk. To make matters worse, adversaries understand this complexity and leverage the ambiguous nature of the IP reputation to exploit further unsuspecting Internet users or devices connected to the Internet. In addition, traditional techniques like dirty-listing cannot react quickly enough to changes in the security climate, nor can they scale large enough to detect new exploits that may be created and disappear in minutes. In this paper, we introduce the use of cross-protocol analysis and graph neural networks (GNNs) in semi-supervised learning to address the speed and scalability of assessing IP reputation. In the cross-protocol supervised approach, we combine features from the web, email, and domain name system (DNS) protocols to identify ones which are the most useful in discriminating suspicious and benign IPs. In our second experiment, we leverage the most discriminant features and incorporate them into the graph as nodes' features. We use GNNs to pass messages from node to node, propagating the signal to the neighbors while also gaining the benefit of having the originating nodes being influenced by neighboring nodes. Thanks to the relational graph structure we can use only a small portion of labeled data and train the algorithm in a semi-supervised approach. Our dataset represents real-world data that is sparse and only contain a small percentage of IPs with verified clean or suspicious labels but are connected. The experimental results demonstrate that the system can achieve 85.28 % accuracy in detecting malicious IP addresses at scale with only 5 % of labeled data.

COVID-19 contagion forecasting framework based on curve decomposition and evolutionary artificial neural networks: A case study in Andalusia, Spain.

Many types of research have been carried out with the aim of combating the COVID-19 pandemic since the first outbreak was detected in Wuhan, China. Anticipating the evolution of an outbreak helps to devise suitable economic, social and health care strategies to mitigate the effects of the virus. For this reason, predicting the SARS-CoV-2 transmission rate has become one of the most important and challenging problems of the past months. In this paper, we apply a two-stage mid and long-term forecasting framework to the epidemic situation in eight districts of Andalusia, Spain. First, an analytical procedure is performed iteratively to fit polynomial curves to the cumulative curve of contagions. Then, the extracted information is used for estimating the parameters and structure of an evolutionary artificial neural network with hybrid architectures (i.e., with different basis functions for the hidden nodes) while considering single and simultaneous time horizon estimations. The results obtained demonstrate that including polynomial information extracted during the training stage significantly improves the mid- and long-term estimations in seven of the eight considered districts. The increase in average accuracy (for the joint mid- and long-term horizon forecasts) is 37.61% and 35.53% when considering the single and simultaneous forecast approaches, respectively.

Tire Ground Rubber Biodegradation by a Consortium Isolated from an Aged Tire.

Rubber is a natural product, the main car tire component. Due to the characteristics acquired by this material after its vulcanization process, its degradation under natural conditions requires very long times, causing several environmental problems. In the present work, the existence of a bacterial consortium isolated from a discarded tire found within the Socabaya River with the ability to degrade shredded tire rubber without any chemical pretreatment is explored. Taking into consideration the complex chemical composition of a rubber tire and the described benefits of the use of pretreatments, the study is developed as a preliminary analysis. The augmentative growth technique was used, and the level of degradation was quantified as a percentage through the analysis of microbial respiration. Schiff's test and the use of comparative photographs of scanning electron microscopy (SEM) were also used. The consortium using next generation genetic sequencing was analyzed. A 4.94% degradation point was obtained after 20 days of experimentation, and it was found that the consortium was mostly made up with Delftia tsuruhatensis with 69.12% of the total genetic readings of the consortium and the existence of 15% of unidentified microbial strains at the genre level. The role played by the organisms in the degradation process is unknown. However, the positive results in the tests carried out show that the consortium had action on the shredded tire, showing a mineralization process.

Combination of Six Individual Derivatives of the Pom-1 Antibiofilm Peptide Doubles Their Efficacy against Invasive and Multi-Resistant Clinical Isolates of the Pathogenic Yeast Candida albicans

In previous studies, derivatives of the peptide Pom-1, which was originally extracted from the freshwater mollusk Pomacea poeyana, showed an exceptional ability to specifically inhibit biofilm formation of the laboratory strain ATCC 90028 as a model strain of the pathogenic yeast Candida albicans. In follow-up, here, we demonstrate that the derivatives Pom-1A to Pom-1F are also active against biofilms of invasive clinical C. albicans isolates, including strains resistant against fluconazole and/or amphotericin B. However, efficacy varied strongly between the isolates, as indicated by large deviations in the experiments. This lack of robustness could be efficiently bypassed by using mixtures of all peptides. These mixed peptide preparations were active against biofilm formation of all the isolates with uniform efficacies, and the total peptide concentration could be halved compared to the original MIC of the individual peptides (2.5 µg/mL). Moreover, mixing the individual peptides restored the antifungal effect of fluconazole against fluconazole-resistant isolates even at 50% of the standard therapeutic concentration. Without having elucidated the reason for these synergistic effects of the peptides yet, both the gain of efficacy and the considerable increase in efficiency by combining the peptides indicate that Pom-1 and its derivatives in suitable formulations may play an important role as new antibiofilm antimycotics in the fight against invasive clinical infections with (multi-) resistant C. albicans.

Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques.

The new pandemic caused by the COVID-19 virus has generated an overload in the quality of medical care in clinical centers around the world. Causes that originate this fact include lack of medical personnel, infrastructure, medicines, among others. The rapid and exponential increase in the number of patients infected by COVID-19 has required an efficient and speedy prediction of possible infections and their consequences with the purpose of reducing the health care quality overload. Therefore, intelligent models are developed and employed to support medical personnel, allowing them to give a more effective diagnosis about the health status of patients infected by COVID-19. This paper aims to propose an alternative algorithmic analysis for predicting the health status of patients infected with COVID-19 in Mexico. Different prediction models such as KNN, logistic regression, random forests, ANN and majority vote were evaluated and compared. The models use risk factors as variables to predict the mortality of patients from COVID-19. The most successful scheme is the proposed ANN-based model, which obtained an accuracy of 90% and an F1 score of 89.64%. Data analysis reveals that pneumonia, advanced age and intubation requirement are the risk factors with the greatest influence on death caused by virus in Mexico.

Development of a Predictive Model for Agave Prices Employing Environmental, Economic, and Social Factors: Towards a Planned Supply Chain for Agave-Tequila Industry.

The interest of consumers to acquire Tequila has caused an increase in its sales. As demand increases, the Tequila industry must obtain its raw material at a constant rate and agave farmers must be prepared to satisfy this supply chain. Because of this, modernization of the strategies used to ensure a planned, scheduled, timely, and predictable production will allow farmers to maintain the current demand for Tequila. This has been evidenced in official historical records from 1999 to 2020 where there is a fluctuation in the price of agave due to supply and demand. Given this scenario, this research shows the development of a multivariable predictive mathematical model that will permit the agave−Tequila production chain to work based on a smart implementation of planned actions to guarantee the agave supply to the Tequila industry. The proposed model has a goodness of fit (R = 0.8676; R¯2 = 0.8609; F(1,20) = 131.01 > F0.01 (1,20) = 8.10) and demonstrates the impact on agave prices is due to several factors: Tequila exports (α = 0.50) > agave plants harvested "jima" (α = 0.44) > dollar exchange (α = 0.43) > Tequila production (α = 0.06) > annual accumulated precipitation (α = 0.05). Nevertheless, the price forecast can be influenced by climate change or economic crises that affect the supply chain. In conclusion, a prediction of agave price stabilization for five years is shown where authorized producers can evaluate future scenarios so that the agave supply chain can be guaranteed for Tequila production, facilitating the decision making regarding its raw material.