Background. The 15q11.1-13.1 duplication, also known as Dup15q syndrome, is a rare congenital disease affecting 1 in 30,000 to 1 in 60,000 children worldwide. This condition is characterized by the presence of at least one extra copy of genetical material within the Prader-Willi/Angelman Critical Region (PWACR) of the referred 15q11.2-q13.1 chromosome. Case Report. Our study presents the clinical and genetical features of the first patient with a denovo 15q11.2 interstitial duplication on the maternal allele (inv Dup15q) that mimics a milder Prader-Willi syndrome probably due to an atypical disruption of the SNHG14 gene. Methylation-specific MLPA analysis has confirmed the presence of a very unlikely duplication that lies between breakpoint 1 (BP1) and the middle of BP2 and BP3 (BP3). This atypical alteration might be linked to the milder patient’s clinical phenotype. Conclusions. This is the first Dup15q patient reported in Ecuador and of the very few in South America. This aberration has never been described in a patient with Dup15q, and the unusual clinical presentation is probably due to the atypical distal breakpoint occurring within the gene SNHG14 which lies between BP2 and BP3 and does not therefore contain the whole PWACR. If the duplication disrupted the gene, then it is possible that it is the cause of, or contributing to, the patient’s clinical phenotype.

Hypertension is responsible for more than two million deaths due to cardiovascular disease annually in Latin America (LATAM), of which one million occurs before 70 years of age. Hypertension is the main risk factor for cardiovascular morbidity and mortality, affecting between 20 and 40% of LATAM adults. Since the publication of the 2017 LASH hypertension guidelines, reports from different LATAM countries have confirmed the burden of hypertension on cardiovascular disease events and mortality in the region. Many studies in the region have reported and emphasized the dramatically insufficient blood pressure control. The extremely low rates of awareness, treatment, and control of hypertension, particularly in patients with metabolic disorders, is a recognized severe problem in LATAM. Earlier implementation of antihypertensive interventions and management of all cardiovascular risk factors is the recognized best strategy to improve the natural history of cardiovascular disease in LATAM. The 2024 LASH guidelines have been developed by a large group of experts from internal medicine, cardiology, nephrology, endocrinology, general medicine, geriatrics, pharmacology, and epidemiology of different countries of LATAM and Europe. A careful search for novel studies on hypertension and related diseases in LATAM, together with the new evidence that emerged since the 2017 LASH guidelines, support all statements and recommendations. This update aims to provide clear, concise, accessible, and useful recommendations for health professionals to improve awareness, treatment, and control of hypertension and associated cardiovascular risk factors in the region.

About 8% of the Ecuadorian population suffers some type of amputation of upper or lower limbs. Due to the high cost of a prosthesis and the fact that the salary of an average worker in the country reached 248 USD in August 2021, they experience a great labor disadvantage and only 17% of them are employed. Thanks to advances in 3D printing and the accessibility of bioelectric sensors, it is now possible to create economically accessible proposals. This work proposes the design of a hand prosthesis that uses electromyography (EMG) signals and neural networks for real-time control. The integrated system has a mechanical and electronic design, and the latter integrates artificial intelligence for control. To train the algorithm, an experimental methodology was developed to record muscle activity in upper extremities associated with specific tasks, using three EMG surface sensors. These data were used to train a five-layer neural network. the trained model was compressed and exported using TensorflowLite. The prosthesis consisted of a gripper and a pivot base, which were designed in Fusion 360 considering the movement restrictions and the maximum loads. It was actuated in real time thanks to the design of an electronic circuit that used an ESP32 development board, which was responsible for recording, processing and classifying the EMG signals associated with a motor intention, and to actuate the hand prosthesis. As a result of this work, a database with 60 electromyographic activity records from three tasks was released. The classification algorithm was able to detect the three muscle tasks with an accuracy of 78.67% and a response time of 80 ms. Finally, the 3D printed prosthesis was able to support a weight of 500 g with a safety factor equal to 15.

Agro-industrial residue valorization under the umbrella of the circular bioeconomy (CBE) has prompted the search for further forward-thinking alternatives that encourage the mitigation of the industry’s environmental footprint. From this perspective, second-life valorization (viz., thermoplastic composites) has been explored for agro-industrial waste (viz., oil palm empty fruit bunch fibers, OPEFBFs) that has already been used previously in other circular applications (viz., the removal of domestic wastewater contaminants). Particularly, this ongoing study evaluated the performance of raw residues (R-OPEFBFs) within three different size ranges (250–425, 425–600, 600–800 µm) both before and after their utilization in biofiltration processes (as post-adsorbents, P-OPEFBFs) to reinforce a polymer matrix of acrylic resin. The research examined the changes in R-OPEFBF composition and morphology caused by microorganisms in the biofilters and their impact on the mechanical properties of the composites. Smaller R-OPEFBFs (250–425 µm) demonstrated superior mechanical performance. Additionally, the composites with P-OPEFBFs displayed significant enhancements in their mechanical properties (3.9–40.3%) compared to those with R-OPEFBFs. The combination of the three fiber sizes improved the mechanical behavior of the composites, indicating the potential for both R-OPEFBFs and P-OPEFBFs as reinforcement materials in composite applications.

Water popularly termed the ‘The Elixir of Life’ is now polluted beyond control in several regions. Microplastics, the tiny contaminants have found their way into all walks of life. They have also been found to be present in human blood, multiple organs, and even breast milk. There is an abundance of microplastics in the air we breathe, the food we eat, and the water we drink. Curbing them has to start with a ban of all forms of primary microplastics, and single use plastics with preference being given to biodegradable alternatives. India in particular banned single use plastics in 2022, which put an end to several commonly used plastic items being replaced with biodegradables. Paint is one of the largest contributors to microplastics, followed by textile industry, cosmetic, pharmaceutical industry, packaging industry are all top contributors to microplastics. The wastewater treatment plants aren't designed to filter microplastics from the source and this results in microplastics polluting all water resources. Though several novel techniques for microplastic segregation exist such as sieving, filtration, density separation, visual sorting, alkali digestion exist, they aren't fully employed as the initial process of microplastic segregation from waste is still in question. ©

Analysis of the human gait represents a fundamental area of investigation within the broader domains of biomechanics, clinical research, and numerous other interdisciplinary fields. The progression of visual sensor technology and machine learning algorithms has enabled substantial developments in the creation of human gait analysis systems. This paper presents a comprehensive review of the advancements and recent findings in the field of vision-based human gait analysis systems over the past five years, with a special emphasis on the role of vision sensors, machine learning algorithms, and technological innovations. The relevant papers were subjected to analysis using the PRISMA method, and 72 articles that met the criteria for this research project were identified. A detailing of the most commonly used visual sensor systems, machine learning algorithms, human gait analysis parameters, optimal camera placement, and gait parameter extraction methods is presented in the analysis. The findings of this research indicate that non-invasive depth cameras are gaining increasing popularity within this field. Furthermore, depth learning algorithms, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, are being employed with increasing frequency. This review seeks to establish the foundations for future innovations that will facilitate the development of more effective, versatile, and user-friendly gait analysis tools, with the potential to significantly enhance human mobility, health, and overall quality of life. This work was supported by [GOBIERNO DE ESPANA/PID2023-150967OB-I00].

While groundwater is commonly perceived as safe, the excessive presence of trace metals, particularly arsenic (As), can pose significant health hazards. This review examines the current scenario of pollutants and their mitigations focusing on As contamination in groundwater across multiple nations, with a specific emphasis on the Indian Peninsula. Arsenic pollution surpasses the WHO limit of 10 ppb in 107 countries, impacting around 230 million people worldwide, with a substantial portion in Asia, including 20 states and four union territories in India. Analysis of the correlation between the aquifer and arsenic poisoning highlights severe contamination in groundwater originating from loose sedimentary aquifer strata, particularly in recently formed mountain ranges with geological sources presumed to contribute over 90% of arsenic pollution, i.e. a big environmental challenge. A myriad of techniques, including chromatographic, electrochemical, biological, spectroscopic, and colorimetric methods among others, are available for the detection and removal of arsenic from groundwater. Removal strategies encompass a wide array of approaches such as bioremediation, adsorption, coagulation/flocculation, ion exchange, biological processes, membrane treatment, and oxidation techniques specifically tailored for affected areas. Constructed wetlands help to eliminate heavy metal impurities such as As, Zn, Cd, Cu, Ni, Fe, and Cr. Their efficiency is influenced by design and environmental factors. Nanotechnology and nanoparticles have recently been studied to remove arsenic and toxic metal ions from water. Cost-effective solutions including community-based mitigation initiatives, alongside policy and regulatory frameworks addressing arsenic contamination, are essential considerations.

Recent practices in textile supply chains (SC) show a growing concern for sustainability not only in its economic dimension, but fundamentally in its environmental and social ones. One of the key management processes that affect sustainability is the SC operations planning since its fundamental role in achieving a balance between supply and demand in a sustainable manner. Moreover, in an uncertain and dynamic environment such as the textile sector, it is necessary to provide a certain learning capability to the operations planning techniques used to increase the speed and quality of response of the textile SC to unexpected situations. In this context, mathematical programming models, heuristics and artificial intelligence techniques have proven their validity to achieve sustainable, robust and smart supply chains. Despite their potential, neither a conceptual framework (CF) nor a literature review have been detected to support the development and study of such models in the textile supply chain operations planning. In view of these gaps, this paper proposes a CF for supporting the sustainable and smart operations planning of the textile supply chains in a dynamic and uncertain context based on a set of dimensions, categories and elements that reflect the specific characteristics of the textile sector. Firstly, a tentative CF is predefined based on other generic works on SC operations planning in uncertain context and the own authors’ knowledge. Secondly, a structured literature review based on this CF has been made resulting, at the same time, in the updating of some of its dimensions, categories and elements to reflect some textile specific characteristics. Consequently, the CF is not only predefined but also logically derived from the literature analysis. The results of the literature review show that there is a great opportunity to contribute to making textile supply chains more sustainable, smart, flexible, robust and resilient in dynamic and uncertain environments.

AbstractBackgroundAnaplastic astrocytoma is a rare disorder in children from 10 to 14 years of age, with an estimated 0.38 new cases per 100,000 people per year worldwide. Panel-based next-generation sequencing opens new possibilities for diagnosis and therapy of rare diseases such as this one. Because it has never been genetically studied in the Ecuadorian population, we chose to genetically characterize an Ecuadorian pediatric patient with anaplastic astrocytoma for the first time. Doing so allows us to provide new insights into anaplastic astrocytoma diagnosis and treatment.Case presentationOur patient was a 13-year-old Mestizo girl with an extensive family history of cancer who was diagnosed with anaplastic astrocytoma. According to ClinVar, SIFT, and PolyPhen, the patient harbored 354 genomic alterations in 100 genes. These variants were mostly implicated in deoxyribonucleic acid (DNA) repair. The top five most altered genes wereFANCD2,NF1,FANCA,FANCI, andWRN.Even thoughTP53presented only five mutations, the rs11540652 single-nucleotide polymorphism classified as pathogenic was found in the patient and her relatives; interestingly, several reports have related it to Li-Fraumeni syndrome. Furthermore,in silicoanalysis using the Open Targets Platform revealed two clinical trials for pediatric anaplastic astrocytoma (studying cabozantinib, ribociclib, and everolimus) and 118 drugs that target the patient’s variants, but the studies were not designed specifically to treat pediatric anaplastic astrocytoma.ConclusionsNext-generation sequencing allows genomic characterization of rare diseases; for instance, this study unraveled a pathogenic single-nucleotide polymorphism related to Li-Fraumeni syndrome and identified possible new drugs that specifically target the patient’s variants. Molecular tools should be implemented in routine clinical practice for early detection and effective preemptive intervention delivery and treatment.

This analytical paper investigates the impact of initial electric potential as well as graphene origami characteristics on the deformable/foldable behavior of the sandwich double curved shell composed of a graphene origami-reinforced copper core sandwich by intelligent layers at top/bottom as sensor, actuator and energy harvester. The stretched and foldable model is extended for the description of the kinematic and constitutive relations, respectively. The variational-based formulation is extended for the derivation of the governing equations. The solution procedure is extended for the special boundary conditions to seek the impact of multi-field loading and material constituent characteristics on the electro-thermo-folding responses of the sandwich hybrid shell. As a quantitative result, one can arrive at 10% decrease in deflection with 2% addition of graphene origami reinforcement.

Osteosarcoma is the most common type of primary malignant bone tumor. Although nowadays 5-year survival rates can reach up to 60–70%, acute complications and late effects of osteosarcoma therapy are two of the limiting factors in treatments. We developed a multi-objective algorithm for the repurposing of new anti-osteosarcoma drugs, based on the modeling of molecules with described activity for HOS, MG63, SAOS2, and U2OS cell lines in the ChEMBL database. Several predictive models were obtained for each cell line and those with accuracy greater than 0.8 were integrated into a desirability function for the final multi-objective model. An exhaustive exploration of model combinations was carried out to obtain the best multi-objective model in virtual screening. For the top 1% of the screened list, the final model showed a BEDROC = 0.562, EF = 27.6, and AUC = 0.653. The repositioning was performed on 2218 molecules described in DrugBank. Within the top-ranked drugs, we found: temsirolimus, paclitaxel, sirolimus, everolimus, and cabazitaxel, which are antineoplastic drugs described in clinical trials for cancer in general. Interestingly, we found several broad-spectrum antibiotics and antiretroviral agents. This powerful model predicts several drugs that should be studied in depth to find new chemotherapy regimens and to propose new strategies for osteosarcoma treatment.