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Diatoms are major players in the global carbon cycle, and their metabolism is affected by ocean conditions. Understanding the impact of changing inorganic nutrients in the oceans on diatoms is crucial, given the changes in global carbon dioxide levels. Here, we present a genome-scale metabolic model (iMK1961) for Cylindrotheca closterium, an in silico resource to understand uncharacterized metabolic functions in this ubiquitous diatom. iMK1961 represents the largest diatom metabolic model to date, comprising 1961 open reading frames and 6718 reactions. With iMK1961, we identified the metabolic response signature to cope with drastic changes in growth conditions. Comparing model predictions with Tara Oceans transcriptomics data unraveled C. closteriums metabolism in situ. Unexpectedly, the diatom only grows photoautotrophically in 21% of the sunlit ocean samples, while the majority of the samples indicate a mixotrophic (71%) or, in some cases, even a heterotrophic (8%) lifestyle in the light. Our findings highlight C. closteriums metabolic flexibility and its potential role in global carbon cycling.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) can lead to cardiovascular complications. We aimed to understand the trends in admission for COVID-19 and the incidence of various cardiovascular events. MATERIAL AND METHODS: The 2020 and 2021 National Inpatient Sample (NIS) was studied for cases of COVID-19 between April 2020 and December 2021 in the United States. Linear-by-linear association helped us understand the trends of various events. RESULTS: The number of cases of COVID-19 was highest in January 2021 (261,469 patients). The incidence of acute pulmonary embolism rose from 2.08% in April 2020 to 4.82% in November 2021, while deep vein thrombosis cases rose from 1.74% in April 2020 to 2.63% in December 2021. The incidence of cardiac arrest varied, with a maximum of 3.00% in August 2021. Similarly, acute ischemic stroke cases experienced their highest incidence in January 2021 (0.91%). The incidence of myocarditis was highest in April and May 2020 (0.42% each). Peak takotsubo cases were seen between October and December 2021. The highest overall all-cause mortality among COVID-19 cases was seen in April 2020 (16.74%). CONCLUSIONS: Throughout the 21 months of our analysis, various trends in COVID-19 cases and incidence of cardiac events were noticed. This could relate to the different variants of COVID-19, their direct and indirect impact on coagulation pathways and the myocardial tissues, and the protective roles of the vaccines.

Abstract Introduction Advanced technologies in radiation treatment deliveries require high accuracy and precision in setup before treatment to get accurate dose delivery for planned target volumes. During imaging procedures, patients are getting extra doses from kilovoltage computed tomography (KVCT) and megavoltage computed tomography (MVCT). Computed tomography dose index is an important dosimetric parameter to know the degree of radiation-induced side effects. Aim The main aim of this study is to quantify the dose received by patients during MVCT imaging procedures, weighted computed tomography dose index (CTDIw), volume computed tomography dose index (CTDIvol) and dose length product (DLP). Methods and materials This study has been performed on Radixact X9 LINAC machine with 6MV flattening filter-free (FFF) photon beam for treatment and imaging with 3.5 MeV average photon energy. CTDIw and dose length product (DLP) have been calculated for different scanning lengths 18 cm, 10 cm and 0.7 cm with fine, normal and coarse modes with standard imaging A1SL1 ion chamber, cheese phantom and tomoelectrometer. Results CTDIw, CTDIvol, and DLP have been calculated with cheese phantom using A1SL1 ion chamber. CTDIw for 0.7 cm is 2.16 cGy, 1.14 cGy and 0.77 cGy for fine normal and coarse modes, respectively. Conclusion CTDIw is a dosimetric index to know the dose levels at center and periphery of patient according to scan length. It gives an idea about how much additional dose is received by the patient during the imaging procedures. High pitch gives the lesser absorbed dose. DLP gives the knowledge about stochastic effects from radiation to patients after MVCT.

An 8.5-year-old boy presented with a history of progressive unilateral foot drop. A contrast magnetic resonance imaging of the pelvic girdle and the lumbosacral plexus revealed a diffuse fusiform thickening of the right lumbosacral trunk and the sciatic nerve in the intrapelvic and gluteal regions, with findings characteristic of a choristoma. Clinical and radiologic features as well as complications of this unusual entity are described.

Background: Treating with curative intent elderly patients affected by acute lymphoblastic leukemia (ALL) is an unmet clinical need. Comorbidities, lower tolerance to organ toxicities, and poor disease-related prognostic features make it difficult to cure these patients. Objectives: To analyze the outcome of ALL elderly patients aged over 55 years treated intensively or with the best supportive care (BSC) over the last 20 years.

Introduction: The present study includes a qualitative analysis of skin entry exit doses and Integral doses (ID) utilizing various treatment planning strategies. Material and Methods: For this study, we have taken 20 patients suffering from medulloblastoma and planned for Craniospinal irradiation (CSI) with Helical Intensity Modulated radiation therapy HIMRT, Helical three dimensional conformal radiation therapy H3DCRT, and Volumetric modulated arc therapy VMAT treatment planning techniques on Accuray Radixact X9 and Elekta Synergy (Agility) linear accelerator. Results: Various dosimetric parameters like Dmax, Dmean, D80%, D50%, D30%, and D10% have been calculated for skin and skin_ring. Integral doses have been calculated for PTV-Brain and PTV-Spine, taking into consideration at least V95% (target coverage by 95% of the prescribed dose). Statistical analysis was done using SPS software version 22. Mean skin entry and exit doses were [HIMRT=7.07±0.703, H3DCRT = 7.87±0.958 VMAT=4.09±0.706]. For Phase-1mean integral dose for PTV_Brain and PTV_Spine was [HIMRT=11.37±4.458, H3DCRT=12.30±5.00, VMAT=7.21±2.990] and Phase-2 mean integral doses for GTV_Boost was [HIMRT=0.10±0.10 H3DCRT = 0.11±0.117 VMAT=0.03±0.067]. Skin entry exit doses and integral doses were observed to be highest in H3DCRT and then HIMRT. Meanwhile, VMAT plans show minimum entry exit doses and integral doses for Phase 1 and Phase 2. Conclusion: This study concludes that VMAT treatment planning techniques showing less skin entry exit doses and integral doses compared with helical treatment planning techniques. Taking care of these factors can give better clinical outcomes in terms of less late side effects.

Antecedentes y objetivos: La carga de enfermedad y los costos asociados al consumo de alcohol son un problema de salud pública. Formas eficaces de combatir este problema son la detección individual y la consejería breve. El objetivo de este estudio es estimar y describir la cobertura nacional de consulta sobre consumo de alcohol (CCA) dirigida por profesionales de salud en Chile. Métodos: Se incluyó una muestra nacional aleatoria de 5240 participantes de la Encuesta Nacional de Salud de Chile 2017. Las estimaciones fueron ponderadas según diferencias en la probabilidad de selección y las tasas de falta de respuesta. Se realizaron regresiones logísticas univariadas por edad, sexo, zona y región, ajustadas por confundentes. Resultados: La prevalencia nacional de CCA fue de 30 % (IC 95 %: 27,8-32,5), 31,6 % (IC 95 %: 28,1-35,3) y 28,8 % (IC 95%: 25,7-31,9) para hombres y mujeres respectivamente. La prevalencia más baja fue en participantes de 15 a 24 años (25,1%, IC 95%: 19,8-31,1) y la más alta (33%, IC 95%: 29,5-37,3) en la población de 45 a 64 años. En los modelos de regresión, la edad (OR: 1,06, IC 95% 1,00-1,11, por diez años de aumento en la edad) y el uso de drogas hepatotóxicas (OR: 1,45, IC 95%: 1,10-1,90), fueron las únicas variables que se asociaron significativamente con mayor CCA. Conclusión: Este estudio encontró bajos porcentajes de CCA en Chile. Hace falta estudiar a mayor profundidad esta problemática para la mejoría o generación de nuevas políticas.

Background: Cognitive alterations have been reported in early stages of psychosis including people with First Episode Psychosis (FEP), Clinical High-Risk Mental State (CHR), and Psychotic-Like Experience (PLE). This study aimed to compare the cognitive function in early stages of psychosis using the Montreal Cognitive Assessment (MoCA), a low-cost and brief assessment tool of cognitive functions. Methods: A total of 154 individuals, including 35 with FEP, 38 CHR, 44 PLE, and 37 healthy controls (HC), were evaluated with the MoCA in Santiago, Chile. We calculated the mean total score of the MoCA and the standard deviation of the mean. Groups were assessed for a trend to lower scores in a pre-determined sequence (HC > PLE > CHR > FEP) using the Jonckheere-Terpstra test (TJT). Results: The mean total MoCA scores were 24.8 ± 3.3 in FEP, 26.4 ± 2.4 in CHR, 26.4 ± 2.3 in PLE, and 27.2 ± 1.8 in HC. The analyses revealed a significant trend (p PLE > CHR > FEP. The mean total scores of all groups were above the cut-off for cognitive impairment (22 points). Conclusions: The MoCA describes lower scores in cognition across early stages of psychosis and may be a useful low-cost assessment instrument in early intervention centers of poorly resourced settings.

The transformation from physical network function to Virtual Network Function (VNF) requires a fundamental design change in how applications and services are tested and assured in a hybrid virtual network. Once the VNFs are onboarded in a cloud network infrastructure, operators need to test VNFs in real-time at the time of instantiation automatically. This paper explicitly analyses the problem of adaptive self-healing of a Virtual Machine (VM) allocated by the VNF with the Deep Reinforcement Learning (DRL) approach. The DRL-based big data collection and analytics engine performs aggregation to probe and analyze data for troubleshooting and performance management. This engine helps to determine corrective actions (self-healing), such as scaling or migrating VNFs. Hence, we proposed a Deep Queue Learning (DQL) based Deep Queue Networks (DQN) mechanism for self-healing VNFs in the virtualized infrastructure manager. Virtual network probes of closed-loop orchestration perform the automation of the VNF and provide analytics for real-time, policy-driven orchestration in an open networking automation platform through the stochastic gradient descent method for VNF service assurance and network reliability. The proposed DQN/DDQN mechanism optimizes the price and lowers the cost by 18% for resource usage without disrupting the Quality of Service (QoS) provided by the VNF. The outcome of adaptive self-healing of the VNFs enhances the computational performance by 27% compared to other state-of-the-art algorithms.

In this paper, we propose a hybrid method for detecting fake accounts on Instagram by using the Binary Grey Wolf Optimization (BGWO) and Particle Swarm Optimization (PSO) algorithms. By combining these two algorithms, we aim to leverage their complementary strengths and enhance the overall optimization performance. We evaluate the proposed hybrid method using four classifiers: Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR). The dataset for the experiments contains 65,329 Instagram accounts. We extract features from each account, including profile information, posting behavior, and engagement metrics. The Binary Grey Wolf and Particle Swarm Optimizations, when combined to form a hybrid method (BGWOPSO), improved the performance in accurately detecting fake accounts on Instagram.

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

Hyaluronan (HA) has gained significant attention in cancer research for its role in modulating chemoresistance. This review aims to elucidate the mechanisms by which HA contributes to chemoresistance, focusing on its interactions within the tumor microenvironment. HA is abundantly present in the extracellular matrix (ECM) and binds to cell-surface receptors such as CD44 and RHAMM. These interactions activate various signaling pathways, including PI3K/Akt, MAPK, and NF-κB, which are implicated in cell survival, proliferation, and drug resistance. HA also influences the physical properties of the tumor stroma, enhancing its density and reducing drug penetration. Additionally, HA-mediated signaling contributes to the epithelial–mesenchymal transition (EMT), a process associated with increased metastatic potential and resistance to apoptosis. Emerging therapeutic strategies aim to counteract HA-induced chemoresistance by targeting HA synthesis, degradation, metabolism, or its binding to CD44. This review underscores the complexity of HA’s role in chemoresistance and highlights the potential for HA-targeted therapies to improve the efficacy of conventional chemotherapeutics.

El eje central de este artículo es analizar y caracterizar las pautas de evaluación solicitadas por el programa Sello Migrante del Servicio Nacional de Migraciones de Chile a las comunas que han sido reconocidas con el certificado Sello Migrante. Para tales efectos, se evalúan todas las comunas que han participado de la iniciativa, desde su inicio en diciembre de 2015 hasta diciembre de 2022. En los casos de los municipios revalidados, se considera la evaluación del último informe remitido. Los criterios de evaluación por parte de la política pública en observación constan de cinco grandes dimensiones; empero, la presente propuesta centra su atención en caracterizar la institucionalidad emergente en los gobiernos locales en los puntos (i) institucionalidad y (ii) capacitaciones. En efecto, se pretende analizar el comportamiento asociado a la implementación de una política pública inédita en el tratamiento de los actuales fenómenos migratorios internacionales, particularmente, el tipo de articulación entre la estructura central del Estado, gobiernos locales certificados y extranjeros residentes en esos territorios. Asimismo, se observan las dificultades de gestión y las ideas que surgen desde algunos municipios para seguir profundizando la nueva gestión pública migratoria existente en el país.

Abstract Background Disruptions in essential health services during the COVID-19 pandemic have been reported in several countries. Yet, patterns in health service disruption according to country responses remain unclear. In this paper, we investigate associations between the stringency of COVID-19 containment policies and disruptions in 31 health services in 10 low- middle- and high-income countries in 2020. Methods Using routine health information systems and administrative data from 10 countries (Chile, Ethiopia, Ghana, Haiti, Lao People’s Democratic Republic, Mexico, Nepal, South Africa, South Korea, and Thailand) we estimated health service disruptions for the period of April to December 2020 by dividing monthly service provision at national levels by the average service provision in the 15 months pre-COVID (January 2019-March 2020). We used the Oxford COVID-19 Government Response Tracker (OxCGRT) index and multi-level linear regression analyses to assess associations between the stringency of restrictions and health service disruptions over nine months. We extended the analysis by examining associations between 11 individual containment or closure policies and health service disruptions. Models were adjusted for COVID caseload, health service category and country GDP and included robust standard errors. Findings Chronic disease care was among the most affected services. Regression analyses revealed that a 10% increase in the mean stringency index was associated with a 3.3 percentage-point (95% CI -3.9, -2.7) reduction in relative service volumes. Among individual policies, curfews, and the presence of a state of emergency, had the largest coefficients and were associated with 14.1 (95% CI -19.6, 8.7) and 10.7 (95% CI -12.7, -8.7) percentage-point lower relative service volumes, respectively. In contrast, number of COVID-19 cases in 2020 was not associated with health service disruptions in any model. Conclusions Although containment policies were crucial in reducing COVID-19 mortality in many contexts, it is important to consider the indirect effects of these restrictions. Strategies to improve the resilience of health systems should be designed to ensure that populations can continue accessing essential health care despite the presence of containment policies during future infectious disease outbreaks.

Nitrogen fixation is an important metabolic process carried out by microorganisms, which converts molecular nitrogen into inorganic nitrogenous compounds such as ammonia (NH3). These nitrogenous compounds are crucial for biogeochemical cycles and for the synthesis of essential biomolecules, i.e. nucleic acids, amino acids and proteins. Azotobacter vinelandii is a bacterial non-photosynthetic model organism to study aerobic nitrogen fixation (diazotrophy) and hydrogen production. Moreover, the diazotroph can produce biopolymers like alginate and polyhydroxybutyrate (PHB) that have important industrial applications. However, many metabolic processes such as partitioning of carbon and nitrogen metabolism in A. vinelandii remain unknown to date. Genome-scale metabolic models (M-models) represent reliable tools to unravel and optimize metabolic functions at genome-scale. M-models are mathematical representations that contain information about genes, reactions, metabolites and their associations. M-models can simulate optimal reaction fluxes under a wide variety of conditions using experimentally determined constraints. Here we report on the development of a M-model of the wild type bacterium A. vinelandii DJ (iDT1278) which consists of 2,003 metabolites, 2,469 reactions, and 1,278 genes. We validated the model using high-throughput phenotypic and physiological data, testing 180 carbon sources and 95 nitrogen sources. iDT1278 was able to achieve an accuracy of 89% and 91% for growth with carbon sources and nitrogen source, respectively. This comprehensive M-model will help to comprehend metabolic processes associated with nitrogen fixation, ammonium assimilation, and production of organic nitrogen in an environmentally important microorganism.

Background: Vitiligo is an amelanotic disorder affecting 0.5% to 2% of the world’s population. Several treatment modalities have been tried and it has been reported that combination therapies augment the repigmentation in vitiligo. Objective: To compare the efficacy of microneedling with 5% 5-FU and microneedling with 0.1% tacrolimus in treatment of vitiligo. Methods: Twenty-five patients with localized stable vitiligo were subjected to microneedling of two patches with dermaroller of size 2 mm, then application of 5-fluorouracil to one patch and tacrolimus on the other patch. This procedure was repeated every month for maximum 6 months (six sessions). The patients were followed up for 3 to 4 months after the last session. Results: The overall repigmentation was slightly higher in 5-FU treated patches than tacrolimus treated patches. Very much improved to much improvement in VASI score was seen in 14 patches (64%) in 5% 5FU treated patches and 11 patches (44%) in 0.1% tacrolimus treated patches. Improved and minimally improved lesions, that is, eight patches (32%) and 11 patches (44%) respectively treated with 5% 5FU and 0.1% tacrolimus. Three patients (12%) showed no improvement. However, there was significant difference between the two drugs, regarding inflammation, ulceration, and hyperpigmentation which occurred with 5-FU. Conclusion: Microneedling combined with 5% 5-fluorouracil or tacrolimus is effective though 5-FU achieved better repigmentation.

Pastures are a vital source of dairy products and cattle nutrition, and as such, play a significant role in New Zealand’s agricultural economy. However, weeds can be a major problem for pastures, making it a challenge for dairy farmers to monitor and control them. Currently, most of the tasks for weed management are done manually, and farmers lack persistent technology for weed control. This motivated us to design, implement, and evaluate a Decision Support System (DSS) to detect weeds in pastures and provide decisions for the cleanup of weeds. Our proposed system uses two primary inputs: weeds and bare patches. We created a synthetic dataset to train a weed detection model and designed a fuzzy inference system to assess a pasture. We also used a neuro-fuzzy system in our DSS to evaluate our fuzzy model and tune its parameters for better functioning and accuracy. Our work aims to assist dairy farmers in better weed monitoring, as well as to provide 2D maps of weed density and yield score, which can be of significant value when no digital and meaningful images of pastures exist. The system can also support farmers in scheduling, recommending prohibitive tasks, and storing historical data for pasture analysis, collaborated by stakeholders.

Abstract COVID-19 has prompted the use of readily available administrative data to track health system performance in times of crisis and to monitor disruptions in essential healthcare services. In this commentary we describe our experience working with these data and lessons learned across countries. Since April 2020, the Quality Evidence for Health System Transformation (QuEST) network has used administrative data and routine health information systems (RHIS) to assess health system performance during COVID-19 in Chile, Ethiopia, Ghana, Haiti, Lao People’s Democratic Republic, Mexico, Nepal, South Africa, Republic of Korea and Thailand. We compiled a large set of indicators related to common health conditions for the purpose of multicountry comparisons. The study compiled 73 indicators. A total of 43% of the indicators compiled pertained to reproductive, maternal, newborn and child health (RMNCH). Only 12% of the indicators were related to hypertension, diabetes or cancer care. We also found few indicators related to mental health services and outcomes within these data systems. Moreover, 72% of the indicators compiled were related to volume of services delivered, 18% to health outcomes and only 10% to the quality of processes of care. While several datasets were complete or near-complete censuses of all health facilities in the country, others excluded some facility types or population groups. In some countries, RHIS did not capture services delivered through non-visit or nonconventional care during COVID-19, such as telemedicine. We propose the following recommendations to improve the analysis of administrative and RHIS data to track health system performance in times of crisis: ensure the scope of health conditions covered is aligned with the burden of disease, increase the number of indicators related to quality of care and health outcomes; incorporate data on nonconventional care such as telehealth; continue improving data quality and expand reporting from private sector facilities; move towards collecting patient-level data through electronic health records to facilitate quality-of-care assessment and equity analyses; implement more resilient and standardized health information technologies; reduce delays and loosen restrictions for researchers to access the data; complement routine data with patient-reported data; and employ mixed methods to better understand the underlying causes of service disruptions.