University of the Valley of Atemajac

Purpose:The purpose of this study was to analyze national trends in knee and shoulder arthroscopy to identify seasonal and annual variability using the Healthcare Cost and Utilization Project-Nationwide Ambulatory Surgery Sample (HCUP-NASS) dataset.Methods:Estimated national rates of ambulatory knee and shoulder arthroscopy were analyzed using HCUP-NASS data from 2016 to 2021. Time trend plots were generated to identify potential seasonal trends in these procedures.Results:There was a decreasing trend in the number of procedures (shoulder arthroscopy, rotator cuff repair [RCR], knee arthroscopy, meniscal repair [MR]/meniscectomy, and anterior cruciate ligament reconstruction [ACLR]) done from 2016 to 2021. There were an estimated 264,987 (95% confidence interval [CI] = 251,205 to 276,770) patients who underwent shoulder arthroscopy and 381,125 (95% CI = 362,555 to 399,696) patients who underwent knee arthroscopy in 2021, as compared with 345,892 (95% CI = 326,224 to 365,559) patients who underwent shoulder arthroscopy and 521,912 (95% CI = 496,905 to 546,919) patients who underwent knee arthroscopy in 2016. Male patients showed markedly higher rates of shoulder arthroscopy, RCR, knee arthroscopy, MR, and ACLR procedures as compared with female patients, whereas female patients showed markedly higher rates of knee arthroscopy done without MR and ACLR. Shoulder and knee arthroscopy rates peaked in the 55 to 75-year-old age group. In addition, both procedures were most frequently done in December and had the lowest utilization in the month of July.Conclusion:There was a nonlinear decrease in the estimates of shoulder arthroscopy, RCR, MR/meniscectomy, knee arthroscopy, and anterior cruciate ligament reconstruction procedures from 2016 to 2021, with peaks in 2016 and 2019, and a low point in 2020. There are notable variations in rates of knee and shoulder arthroscopy procedures by age and sex.Clinical relevance:Recent data on commonly done ambulatory orthopaedic arthroscopies are important for policy makers and for understanding utilization trends.

Pathophysiological Mechanisms, Clinical Spectrum, And Contemporary Strategies in the Diagnosis and Management of Brugada Syndrome: A Comprehensive Review

Restrictive cardiomyopathy represents one of the most challenging phenotypes within the spectrum of myocardial diseases, distinguished by impaired ventricular filling due to increased myocardial stiffness while preserving near-normal systolic function during early stages. Although considered uncommon, it carries a disproportionately high morbidity and mortality burden owing to its subtle clinical onset, heterogeneous etiologies, and frequently delayed diagnosis. Advances in multimodal imaging, myocardial tissue characterization, and molecular diagnostics have reshaped current understanding of this entity, revealing intricate associations with infiltrative, fibrotic, storage, and radiation-induced myocardial injuries. The clinical trajectory is typically dominated by progressive diastolic dysfunction, biatrial enlargement, preserved ejection fraction heart failure physiology, and variable conduction system involvement. Despite these characteristic features, restrictive cardiomyopathy remains diagnostically elusive, often requiring integration of echocardiography, cardiac magnetic resonance, endomyocardial biopsy, and biomarker profiling. Therapeutic strategies continue to evolve, transitioning from purely supportive management to etiology-directed interventions, including novel pharmacologic agents and targeted therapies for infiltrative and genetic subtypes. This article provides an extensive review of the contemporary understanding of restrictive cardiomyopathy, emphasizing its complex pathophysiology, diagnostic challenges, and emerging therapeutic horizons.

The Cognitive Buffer Hypothesis (CBH) posits that larger brains evolved to enhance survival in changing conditions. However, larger brains also carry higher energy demands, imposing additional metabolic burdens. Alongside brain size, brain organization plays a key role in cognitive ability and, with suitable architectures, may help mitigate energy challenges. This study evolves Artificial Neural Networks (ANNs) used by Reinforcement Learning (RL) agents to investigate how environmental variability and energy costs influence the evolution of neural complexity, defined in terms of ANN size and structure. Results indicate that under energy constraints, increasing seasonality led to smaller ANNs. This challenges CBH and supports the Expensive Brain Hypothesis (EBH), as highly seasonal environments reduced net energy intake and thereby constrained brain size. ANN structural complexity primarily emerged as a byproduct of size, where energy costs promoted the evolution of more efficient networks. These results highlight the role of energy constraints in shaping neural complexity, offering in silico support for biological theory and energy-efficient robotic design.

The study evaluated the antioxidant and antibacterial activities of three North East Indian ethnomedicinal vegetables viz., Oxalis corniculata, Portulaca oleraceae, and Polygonum microcephalum by cyclic voltammetry and agar well diffusion method respectively. The antioxidative effects have been evaluated by monitoring the change of the oxidation potential in the redox cycle of 1,4-diaminobenzene in presence of hexane, ethyl acetate and methanol extracts of the plants. 1,4-Diaminobenzene exhibits two reversible redox cycles with E1/2 at 218 mV and 535 mV in DMF with the oxidation waves due to formation of a radical cation and a diiminium dication respectively. In the presence of plant extracts, the oxidation waves were delayed and the radical cation was scavenged, and disappearance of the second oxidant waves in the cyclic voltammograms. Additionally, the methanol extracts demonstrated antibacterial activity against both Gram-positive and Gram-negative bacteria. These findings suggest the potential of these vegetables as natural antioxidants and antibacterial agents.

As doenças cardiovasculares são a principal causa de mortalidade global, sendo a dislipidemia um dos fatores de risco mais relevantes. O tratamento padrão envolve o uso de estatinas, fármacos eficazes na redução do colesterol LDL e na prevenção de eventos cardiovasculares. No entanto, um número significativo de pacientes não atinge as metas terapêuticas ou apresenta intolerância a essas medicações. Nesse contexto, o Inclisiran surge como uma alternativa inovadora, atuando na inibição da PCSK9 por meio da tecnologia de RNA interferente. Este estudo realizou uma revisão de literatura para comparar a eficácia, segurança e aplicabilidade do Inclisiran em relação às estatinas. Os resultados indicam que o Inclisiran reduz o LDL-C em aproximadamente 50%, com efeitos sustentados e administração semestral, favorecendo a adesão ao tratamento. Entretanto, sua efetividade na redução de eventos cardiovasculares ainda não está totalmente comprovada. Por outro lado, as estatinas apresentam benefícios adicionais, como propriedades anti-inflamatórias e estabilização da placa aterosclerótica, além de serem amplamente acessíveis. Conclui-se que o Inclisiran representa uma alternativa promissora, especialmente para pacientes com intolerância às estatinas, mas sua adoção como terapia de primeira linha requer mais evidências clínicas.

This article is the result of a research project that integrates a logistics simulator, which brings together a series of activities to train and strengthen higher education students, particularly in the field of engineering, in disciplinary topics such as forecasting, inventories, production, and process optimization through logistics and administrative decision-making. Using a participatory research methodology with a case study approach, an analysis is conducted on how the Logistic Simulator (called LOST), based on gamification methodology, helps to promote the acquisition of skills from an integrative perspective of complex thinking. The results lead to the identification of decision-making processes carried out through the game features that participants must fulfill to identify the best interrelationships of the defined production variables in the simulator, such as the quantity of pieces to produce, the amount of raw material to consume, and the products to request from suppliers. The findings enable the identification of an interrelationship between decision-making, as experienced through the LOST simulator, and the cultivation of complex logistic decision-making skills in engineering students from nine participating countries. This interrelationship is characterized by the consideration of various combinations, interrelationships, and interdependencies, along with their non-linear effects. Such dynamics can effectively mirror real-world scenarios, striking a balance between efficiency, profitability, and customer satisfaction.