Multifunctional calcium-based nanocarriers for synergistic treatment of triple-negative breast cancer.

Targeted breast cancer therapies hold the potential to improve the efficiency of drug delivery to the pathology site without impacting the viability and function of healthy cells. Herein, we developed multifunctional nanocarriers that target simultaneously several downstream signaling processes in triple negative breast cancer cells. The system comprises pH sensitive CaCO3 nanoparticles (NPs) as carriers of the anticancer drug doxorubicin (DOX). The NPs were coated in a layer-by-layer (LbL) fashion using poly-l-lysine and hyaluronic acid to target receptors overexpressed in breast cancer (e.g. CD44, RHAMM). Spheroids of the triple-negative Hs578T cell line were used as a 3D model to assess the therapeutic potential of this system. Our results showed that the NPs act via a synergistic mechanism that combines Ca2+ overload causing cell calcification and DNA damage by DOX. The LbL coating was crucial for the protection of the healthy cells, i.e. it provides NPs with targeting capacity. The overall data suggests that the LbL-coated NPs loaded with DOX hold great potential for the treatment of breast cancer.

Identification of the needs and priorities of older people and stakeholders in rural and urban areas of Santo Andre, Brazil.

BACKGROUND: There are few data reporting the needs and priorities of older adults in Brazil. This hampers the development and/or implementation of policies aimed at older adults to help them age well. The aim of this study was to understand areas of importance, priorities, enablers and obstacles to healthy ageing as identified by older adults and key stakeholders in both urban and rural environments. METHODS: Two locations were selected, one urban and one rural in the municipality of Santo André, in the metropolitan region of São Paulo (SP). Workshops for older adults (>60 y) and stakeholders were conducted separately in each location. The workshops incorporated an iterative process of discussion, prioritisation and ranking of responses, in roundtable groups and in plenary. Areas of commonality and differences between older adult and stakeholder responses were identified by comparing responses between groups as well as mapping obstacles and enablers to healthy ageing identified by older adults, to the priorities identified by stakeholder groups. The socio-ecologic model was used to categorise responses. RESULTS: There were few shared responses between stakeholders and older adults and little overlap between the top ranked responses of urban and rural groups. With respect to areas of importance, both stakeholder groups ranked policies for older people within their top five reponses. Both older adult groups ranked keeping physically and mentally active, and nurturing spirituality. There was a marked lack of congruence between older adults' obstacles and enablers to healthy ageing and stakeholder priorities, in both urban and rural settings. Most responses were located within the Society domain of the socio-ecologic model, although older adults also responded within the Individual/ Relationships domains, particularly in ranking areas of most importance for healthy ageing. CONCLUSIONS: Our results highlight substantial differences between older adults and stakeholders with respect to areas of importance, priorities, enablers and obstacles to healthy ageing, and point to the need for more engagement between those in advocacy and policymaking roles and the older people whose needs they serve.

Attitudes and perceptions about ageism among nursing students: a scoping review.

OBJECTIVE: to map scientific knowledge on nursing students' attitudes and perceptions regarding ageism. METHOD: scoping review according to the recommendations of the Joanna Briggs Institute. The study question was: What are scientific evidence available on the attitudes and perceptions of nursing students regarding ageism? The search was conducted in 12 databases using the Rayyan application and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. The studies were selected by two reviewers using a data extraction tool. RESULTS: of the 4,595 files, 46 that were published between 1984 and 2022 were selected, and the quantitative method was the most used. The most commonly used instrument was the Kogan's Attitudes Toward Old People Scale. Positive, negative, mixed, neutral, and inconclusive attitudes and perceptions were identified. CONCLUSION: attitudes and perceptions about ageism are diverse and not conclusive. Future intervention studies are recommended to detect changes in the behavior of nursing students in the face of ageism. BACKGROUND: (1) Attitudes and perceptions about ageism are diverse and not conclusive. BACKGROUND: (2) Importance of discussing ageism in the training of nursing students. BACKGROUND: (3) Intervention studies to identify paradigm shifts in nursing students.

Enhancing a deep learning model for pulmonary nodule malignancy risk estimation in chest CT with uncertainty estimation.

OBJECTIVE: To investigate the effect of uncertainty estimation on the performance of a Deep Learning (DL) algorithm for estimating malignancy risk of pulmonary nodules. METHODS AND MATERIALS: In this retrospective study, we integrated an uncertainty estimation method into a previously developed DL algorithm for nodule malignancy risk estimation. Uncertainty thresholds were developed using CT data from the Danish Lung Cancer Screening Trial (DLCST), containing 883 nodules (65 malignant) collected between 2004 and 2010. We used thresholds on the 90th and 95th percentiles of the uncertainty score distribution to categorize nodules into certain and uncertain groups. External validation was performed on clinical CT data from a tertiary academic center containing 374 nodules (207 malignant) collected between 2004 and 2012. DL performance was measured using area under the ROC curve (AUC) for the full set of nodules, for the certain cases and for the uncertain cases. Additionally, nodule characteristics were compared to identify trends for inducing uncertainty. RESULTS: The DL algorithm performed significantly worse in the uncertain group compared to the certain group of DLCST (AUC 0.62 (95% CI: 0.49, 0.76) vs 0.93 (95% CI: 0.88, 0.97); p < .001) and the clinical dataset (AUC 0.62 (95% CI: 0.50, 0.73) vs 0.90 (95% CI: 0.86, 0.94); p < .001). The uncertain group included larger benign nodules as well as more part-solid and non-solid nodules than the certain group. CONCLUSION: The integrated uncertainty estimation showed excellent performance for identifying uncertain cases in which the DL-based nodule malignancy risk estimation algorithm had significantly worse performance. CLINICAL RELEVANCE STATEMENT: Deep Learning algorithms often lack the ability to gauge and communicate uncertainty. For safe clinical implementation, uncertainty estimation is of pivotal importance to identify cases where the deep learning algorithm harbors doubt in its prediction. KEY POINTS: • Deep learning (DL) algorithms often lack uncertainty estimation, which potentially reduce the risk of errors and improve safety during clinical adoption of the DL algorithm. • Uncertainty estimation identifies pulmonary nodules in which the discriminative performance of the DL algorithm is significantly worse. • Uncertainty estimation can further enhance the benefits of the DL algorithm and improve its safety and trustworthiness.

Automated Quantitative Analysis of CT Perfusion to Classify Vascular Phenotypes of Pancreatic Ductal Adenocarcinoma.

CT perfusion (CTP) analysis is difficult to implement in clinical practice. Therefore, we investigated a novel semi-automated CTP AI biomarker and applied it to identify vascular phenotypes of pancreatic ductal adenocarcinoma (PDAC) and evaluate their association with overall survival (OS). METHODS: From January 2018 to November 2022, 107 PDAC patients were prospectively included, who needed to undergo CTP and a diagnostic contrast-enhanced CT (CECT). We developed a semi-automated CTP AI biomarker, through a process that involved deformable image registration, a deep learning segmentation model of tumor and pancreas parenchyma volume, and a trilinear non-parametric CTP curve model to extract the enhancement slope and peak enhancement in segmented tumors and pancreas. The biomarker was validated in terms of its use to predict vascular phenotypes and their association with OS. A receiver operating characteristic (ROC) analysis with five-fold cross-validation was performed. OS was assessed with Kaplan-Meier curves. Differences between phenotypes were tested using the Mann-Whitney U test. RESULTS: The final analysis included 92 patients, in whom 20 tumors (21%) were visually isovascular. The AI biomarker effectively discriminated tumor types, and isovascular tumors showed higher enhancement slopes (2.9 Hounsfield unit HU/s vs. 2.0 HU/s, p < 0.001) and peak enhancement (70 HU vs. 47 HU, p < 0.001); the AUC was 0.86. The AI biomarker's vascular phenotype significantly differed in OS (p < 0.01). CONCLUSIONS: The AI biomarker offers a promising tool for robust CTP analysis. In PDAC, it can distinguish vascular phenotypes with significant OS prognostication.

Bioadhesive Hyaluronic Acid-Based Hydrogels for Spinal Cord Injury.

Spinal cord injuries (SCI) have devastating physical, psychological, and psychosocial consequences for patients. One challenge of nerve tissue repair is the lack of a natural extracellular matrix (ECM) that guides the regenerating axons. Hyaluronic acid (HA) is a major ECM component and plays a fundamental role in facilitating lesion healing. Herein, we developed HA-based adhesive hydrogels by modification of HA with dopamine, a mussel-inspired compound with excellent adhesive properties in an aqueous environment. The hydrogels were loaded with the anti-inflammatory drug ibuprofen and the response of neuronal cells (SH-SY5Y) was evaluated in terms of viability, morphology, and adhesion. The obtained results suggested that the developed materials can bridge lesion gaps, guide axonal growth, and simultaneously act as a vehicle for the delivery of bioactive compounds.

Elevated serum IL-6 levels predict treatment interruption in patients with moderate to severe psoriasis: a 6-year real-world cohort study

Abstract Background: Real-world, primary data on the treatment of psoriasis are scarce, especially concerning the role of soluble biomarkers as outcome predictors. Objective: The authors evaluated the utility of Th1/Th17 serum cytokines along with clinical characteristics as predictors of drug survival in the treatment of psoriasis. Methods: The authors consecutively included participants with moderate to severe psoriasis who were followed up for 6 years. Baseline interferon-α, tumor necrosis factor-α, and inter-leukin (IL)-2, IL-4, IL-6, IL-10, and IL-17A were measured using a cytometric bead array; clinical data were assessed. The authors calculated hazard ratios (HRs) for drug survival using a Cox proportional hazards model. Results: The authors included 262 patients, most of whom used systemic immunosuppressants or biologics. In the multivariate model, poor quality of life measured by the Dermatology Life Quality Index (HR = 1.04; 95% CI 1.01-1.07; p = 0.012) and elevated baseline IL-6 (HR = 1.99; 95% CI 1.29-3.08; p = 0.002) were associated with treatment interruption. Study limitations: The main limitation of any cohort study is the presence of confounders that could not be detected in clinical evaluation. Conclusions: Poor quality of life and elevated baseline serum IL-6 level predicted treatment interruption in patients with moderate to severe psoriasis. Although IL-6 is not the most important mediator of the inflammatory pathway in the skin environment, it is an interesting biomarker candidate for predicting psoriasis treatment response.

Diagnosis of Cutaneous Leishmaniasis Using FTIR Spectroscopy and Machine Learning: An Animal Model Study.

Cutaneous leishmaniasis (CL) is a polymorphic and spectral skin disease caused by Leishmania spp. protozoan parasites. CL is difficult to diagnose because conventional methods are time-consuming, expensive, and low-sensitive. Fourier transform infrared spectroscopy (FTIR) with machine learning (ML) algorithms has been explored as an alternative to achieve fast and accurate results for many disease diagnoses. Besides the high accuracy demonstrated in numerous studies, the spectral variations between infected and noninfected groups are too subtle to be noticed. Since variability in sample set characteristics (such as sex, age, and diet) often leads to significant data variance and limits the comprehensive understanding of spectral characteristics and immune responses, we investigate a novel methodology for diagnosing CL in an animal model study. Blood serum, skin lesions, and draining popliteal lymph node samples were collected from Leishmania (Leishmania) amazonensis-infected BALB/C mice under experimental conditions. The FTIR method and ML algorithms accurately differentiated between infected (CL group) and noninfected (control group) samples. The best overall accuracy (∼72%) was obtained in an external validation test using principal component analysis and support vector machine algorithms in the 1800-700 cm-1 range for blood serum samples. The accuracy achieved in analyzing skin lesions and popliteal lymph node samples was satisfactory; however, notable disparities emerged in the validation tests compared to results obtained from blood samples. This discrepancy is likely attributed to the elevated sample variability resulting from molecular compositional differences. According to the findings, the successful functioning of prediction models is mainly related to data analysis rather than the differences in the molecular composition of the samples.

Machine learning-based radiomic analysis and growth visualization for ablation site recurrence diagnosis in follow-up CT.

OBJECTIVES: Detecting ablation site recurrence (ASR) after thermal ablation remains a challenge for radiologists due to the similarity between tumor recurrence and post-ablative changes. Radiomic analysis and machine learning methods may show additional value in addressing this challenge. The present study primarily sought to determine the efficacy of radiomic analysis in detecting ASR on follow-up computed tomography (CT) scans. The second aim was to develop a visualization tool capable of emphasizing regions of ASR between follow-up scans in individual patients. MATERIALS AND METHODS: Lasso regression and Extreme Gradient Boosting (XGBoost) classifiers were employed for modeling radiomic features extracted from regions of interest delineated by two radiologists. A leave-one-out test (LOOT) was utilized for performance evaluation. A visualization method, creating difference heatmaps (diff-maps) between two follow-up scans, was developed to emphasize regions of growth and thereby highlighting potential ASR. RESULTS: A total of 55 patients, including 20 with and 35 without ASR, were included in the radiomic analysis. The best performing model was achieved by Lasso regression tested with the LOOT approach, reaching an area under the curve (AUC) of 0.97 and an accuracy of 92.73%. The XGBoost classifier demonstrated better performance when trained with all extracted radiomic features than without feature selection, achieving an AUC of 0.93 and an accuracy of 89.09%. The diff-maps correctly highlighted post-ablative liver tumor recurrence in all patients. CONCLUSIONS: Machine learning-based radiomic analysis and growth visualization proved effective in detecting ablation site recurrence on follow-up CT scans.

Elevated serum IL-6 levels predict treatment interruption in patients with moderate to severe psoriasis: a 6-year real-world cohort study.

BACKGROUND: Real-world, primary data on the treatment of psoriasis are scarce, especially concerning the role of soluble biomarkers as outcome predictors. OBJECTIVE: The authors evaluated the utility of Th1/Th17 serum cytokines along with clinical characteristics as predictors of drug survival in the treatment of psoriasis. METHODS: The authors consecutively included participants with moderate to severe psoriasis who were followed up for 6 years. Baseline interferon-α, tumor necrosis factor-α, and interleukin (IL)-2, IL-4, IL-6, IL-10, and IL-17A were measured using a cytometric bead array; clinical data were assessed. The authors calculated hazard ratios (HRs) for drug survival using a Cox proportional hazards model. RESULTS: The authors included 262 patients, most of whom used systemic immunosuppressants or biologics. In the multivariate model, poor quality of life measured by the Dermatology Life Quality Index (HR = 1.04; 95% CI 1.01‒1.07; p = 0.012) and elevated baseline IL-6 (HR = 1.99; 95% CI 1.29‒3.08; p = 0.002) were associated with treatment interruption. STUDY LIMITATIONS: The main limitation of any cohort study is the presence of confounders that could not be detected in clinical evaluation. CONCLUSIONS: Poor quality of life and elevated baseline serum IL-6 level predicted treatment interruption in patients with moderate to severe psoriasis. Although IL-6 is not the most important mediator of the inflammatory pathway in the skin environment, it is an interesting biomarker candidate for predicting psoriasis treatment response.

Attitudes and perceptions about ageism among nursing students: a scoping review / Actitudes y percepciones sobre edadismo entre los estudiantes de enfermería: una revisión de alcance / Atitudes e percepções sobre idadismo em estudantes de enfermagem: revisão de escopo

Objective: to map scientific knowledge on nursing students' attitudes and perceptions regarding ageism. Method: scoping review according to the recommendations of the Joanna Briggs Institute. The study question was: What are scientific evidence available on the attitudes and perceptions of nursing students regarding ageism? The search was conducted in 12 databases using the Rayyan application and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. The studies were selected by two reviewers using a data extraction tool. Results: of the 4,595 files, 46 that were published between 1984 and 2022 were selected, and the quantitative method was the most used. The most commonly used instrument was the Kogan's Attitudes Toward Old People Scale. Positive, negative, mixed, neutral, and inconclusive attitudes and perceptions were identified. Conclusion: attitudes and perceptions about ageism are diverse and not conclusive. Future intervention studies are recommended to detect changes in the behavior of nursing students in the face of ageism.

Objetivo: mapear el conocimiento científico sobre las actitudes y percepciones de los estudiantes de enfermería respecto al edadismo. Método: La pregunta del estudio fue: ¿Qué evidencia científica está disponible sobre las actitudes y percepciones de los estudiantes de enfermería respecto al edadismo? La búsqueda se realizó en 12 bases de datos utilizando la aplicación Rayyan y la Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews . Los estudios fueron seleccionados por dos revisores mediante una herramienta de extracción de datos. Resultados: de los 4.595 artículos, se seleccionaron 46 publicados entre 1984 y 2022, siendo el método cuantitativo el más utilizado. La herramienta más utilizada fue el Kogan's Attitudes Toward Old People Scale . Se identificaron actitudes y percepciones positivas, negativas, mixtas, neutras y no concluyentes. Conclusión: las actitudes y percepciones sobre la discriminación por edad son diversas y no concluyentes. Se recomiendan futuros estudios de intervención para detectar cambios en el comportamiento de los estudiantes de enfermería ante el edadismo.

Objetivo: mapear o conhecimento científico sobre as atitudes e percepções dos estudantes de enfermagem em relação ao idadismo. Método: revisão de escopo segundo as recomendações do Joanna Briggs Institute . A pergunta de estudo foi: Quais são as evidências científicas disponíveis sobre as atitudes e percepções dos estudantes de enfermagem no que diz respeito ao idadismo? A busca foi realizada em 12 bases de dados utilizando o aplicativo Rayyan e o Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews . Realizou-se a seleção dos estudos por dois revisores, com uso de um instrumento para a extração dos dados. Resultados: dos 4.595 arquivos, selecionaram-se 46 publicados entre 1984 e 2022, sendo que a maioria utilizou o método quantitativo. O instrumento mais utilizado foi o Kogan's Attitudes Toward Old People Scale e os estudos mostraram que os estudantes apresentaram atitudes e percepções positivas, negativas, mistas, neutras e não conclusivas. Conclusão: as atitudes e percepções sobre idadismo são diversificadas e não conclusivas. Recomenda-se estudos futuros de intervenção para detecção de mudanças de comportamento dos estudantes de enfermagem frente ao idadismo.

Prediction Variability to Identify Reduced AI Performance in Cancer Diagnosis at MRI and CT.

Background A priori identification of patients at risk of artificial intelligence (AI) failure in diagnosing cancer would contribute to the safer clinical integration of diagnostic algorithms. Purpose To evaluate AI prediction variability as an uncertainty quantification (UQ) metric for identifying cases at risk of AI failure in diagnosing cancer at MRI and CT across different cancer types, data sets, and algorithms. Materials and Methods Multicenter data sets and publicly available AI algorithms from three previous studies that evaluated detection of pancreatic cancer on contrast-enhanced CT images, detection of prostate cancer on MRI scans, and prediction of pulmonary nodule malignancy on low-dose CT images were analyzed retrospectively. Each task's algorithm was extended to generate an uncertainty score based on ensemble prediction variability. AI accuracy percentage and partial area under the receiver operating characteristic curve (pAUC) were compared between certain and uncertain patient groups in a range of percentile thresholds (10%-90%) for the uncertainty score using permutation tests for statistical significance. The pulmonary nodule malignancy prediction algorithm was compared with 11 clinical readers for the certain group (CG) and uncertain group (UG). Results In total, 18 022 images were used for training and 838 images were used for testing. AI diagnostic accuracy was higher for the cases in the CG across all tasks (P < .001). At an 80% threshold of certain predictions, accuracy in the CG was 21%-29% higher than in the UG and 4%-6% higher than in the overall test data sets. The lesion-level pAUC in the CG was 0.25-0.39 higher than in the UG and 0.05-0.08 higher than in the overall test data sets (P < .001). For pulmonary nodule malignancy prediction, accuracy of AI was on par with clinicians for cases in the CG (AI results vs clinician results, 80% [95% CI: 76, 85] vs 78% [95% CI: 70, 87]; P = .07) but worse for cases in the UG (AI results vs clinician results, 50% [95% CI: 37, 64] vs 68% [95% CI: 60, 76]; P < .001). Conclusion An AI-prediction UQ metric consistently identified reduced performance of AI in cancer diagnosis. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Babyn in this issue.

Biocompatible 3D-Printed Tendon/Ligament Scaffolds Based on Polylactic Acid/Graphite Nanoplatelet Composites.

Three-dimensional (3D) printing technology has become a popular tool to produce complex structures. It has great potential in the regenerative medicine field to produce customizable and reproducible scaffolds with high control of dimensions and porosity. This study was focused on the investigation of new biocompatible and biodegradable 3D-printed scaffolds with suitable mechanical properties to assist tendon and ligament regeneration. Polylactic acid (PLA) scaffolds were reinforced with 0.5 wt.% of functionalized graphite nanoplatelets decorated with silver nanoparticles ((f-EG)+Ag). The functionalization of graphene was carried out to strengthen the interface with the polymer. (f-EG)+Ag exhibited antibacterial properties against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli), an important feature for the healing process and prevention of bacterial infections. The scaffolds' structure, biodegradation, and mechanical properties were assessed to confirm their suitability for tendon and ligamentregeneration. All scaffolds exhibited surface nanoroughness created during printing, which was increased by the filler presence. The wet state dynamic mechanical analysis proved that the incorporation of reinforcement led to an increase in the storage modulus, compared with neat PLA. The cytotoxicity assays using L929 fibroblasts showed that the scaffolds were biocompatible. The PLA+[(f-EG)+Ag] scaffolds were also loaded with human tendon-derived cells and showed their capability to maintain the tenogenic commitment with an increase in the gene expression of specific tendon/ligament-related markers. The results demonstrate the potential application of these new 3D-printed nanocomposite scaffolds for tendon and ligament regeneration.

Layer-by-layer coated calcium carbonate nanoparticles for targeting breast cancer cells.

Breast cancer is resistant to conventional treatments due to the specific tumour microenvironment, the associated acidic pH and the overexpression of receptors that enhance cells tumorigenicity. Herein, we optimized the synthesis of acidic resorbable calcium carbonate (CaCO3) nanoparticles and the encapsulation of a low molecular weight model molecule (Rhodamine). The addition of ethylene glycol during the synthetic process resulted in a particle size decrease: we obtained homogeneous CaCO3 particles with an average size of 564 nm. Their negative charge enabled the assembly of layer-by-layer (LbL) coatings with surface-exposed hyaluronic acid (HA), a ligand of tumour-associated receptor CD44. The coating decreased Rhodamine release by two-fold compared to uncoated nanoparticles. We demonstrated the effect of nanoparticles on two breast cancer cell lines with different aggressiveness - SK-BR-3 and the more aggressive MDA-MB-231 - and compared them with the normal breast cell line MCF10A. CaCO3 nanoparticles (coated and uncoated) significantly decreased the metabolic activity of the breast cancer cells. The interactions between LbL-coated nanoparticles and cells depended on HA expression on the cell surface: more particles were observed on the surface of MDA-MB-231 cells, which had the thickest endogenous HA coating. We concluded that CaCO3 nanoparticles are potential candidates to carry low molecular weight chemotherapeutics and deliver them to aggressive breast cancer sites with an HA-abundant pericellular matrix.

Mirtazapine versus Megestrol in the Treatment of Anorexia-Cachexia Syndrome in Patients with Advanced Cancer: A Randomized, Double-Blind, Controlled Phase II Clinical Trial.

This study compared mirtazapine with megestrol in the management of cancer-related anorexia-cachexia syndrome in patients with advanced cancer. A randomized, double-blind, controlled clinical trial involving patients with advanced cancer and anorexia-cachexia syndrome was performed. Participants received mirtazapine 30 mg/day or megestrol 320 mg/day for eight weeks. The primary endpoint was the effect of mirtazapine on weight gain and the secondary endpoints were its effect on appetite, muscle strength, physical performance, body composition, adverse events, and medication adherence. Linear regression model with mixed effects was applied and a significance level of 5% was adopted. Fifty-two patients were randomized. Mean age was 65.8 ± 8.4 years. There was weight gain in 52% of the participants in the megestrol group and in 38% in the mirtazapine group after four weeks (p = 0.040). Appetite improved in 92% of the participants in the megestrol group and in 56% in the mirtazapine group after eight weeks (p = 0.007). In the sub-analysis by sex, women showed improvement in appetite (p < 0.001) and weight gain (p < 0.005) in the mirtazapine group, which was not observed in men. Mirtazapine appears to be inferior to megestrol in weight and appetite improvement. However, there may be a difference in the therapeutic response between sexes.

Cellular allostatic load is linked to increased energy expenditure and accelerated biological aging.

Stress triggers anticipatory physiological responses that promote survival, a phenomenon termed allostasis. However, the chronic activation of energy-dependent allostatic responses results in allostatic load, a dysregulated state that predicts functional decline, accelerates aging, and increases mortality in humans. The energetic cost and cellular basis for the damaging effects of allostatic load have not been defined. Here, by longitudinally profiling three unrelated primary human fibroblast lines across their lifespan, we find that chronic glucocorticoid exposure increases cellular energy expenditure by ∼60%, along with a metabolic shift from glycolysis to mitochondrial oxidative phosphorylation (OxPhos). This state of stress-induced hypermetabolism is linked to mtDNA instability, non-linearly affects age-related cytokines secretion, and accelerates cellular aging based on DNA methylation clocks, telomere shortening rate, and reduced lifespan. Pharmacologically normalizing OxPhos activity while further increasing energy expenditure exacerbates the accelerated aging phenotype, pointing to total energy expenditure as a potential driver of aging dynamics. Together, our findings define bioenergetic and multi-omic recalibrations of stress adaptation, underscoring increased energy expenditure and accelerated cellular aging as interrelated features of cellular allostatic load.

SARS-CoV-2 uses CD4 to infect T helper lymphocytes.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the agent of a major global outbreak of respiratory tract disease known as Coronavirus Disease 2019 (COVID-19). SARS-CoV-2 infects mainly lungs and may cause several immune-related complications, such as lymphocytopenia and cytokine storm, which are associated with the severity of the disease and predict mortality. The mechanism by which SARS-CoV-2 infection may result in immune system dysfunction is still not fully understood. Here, we show that SARS-CoV-2 infects human CD4+ T helper cells, but not CD8+ T cells, and is present in blood and bronchoalveolar lavage T helper cells of severe COVID-19 patients. We demonstrated that SARS-CoV-2 spike glycoprotein (S) directly binds to the CD4 molecule, which in turn mediates the entry of SARS- CoV-2 in T helper cells. This leads to impaired CD4 T cell function and may cause cell death. SARS-CoV-2-infected T helper cells express higher levels of IL-10, which is associated with viral persistence and disease severity. Thus, CD4-mediated SARS-CoV-2 infection of T helper cells may contribute to a poor immune response in COVID-19 patients.

A structural variant at ASIP associated with the darkness of hair coat is found in multiple zebu cattle populations.

A structural variant (SV) in the agouti signaling protein gene (ASIP), namely ASIP-SV1, has been found to strongly correlate with the darkness of hair coat in specific regions of the body of bulls from the zebu (Bos indicus) Nellore breed. Here we visually analyzed the whole-genome sequence of zebu and taurine (Bos taurus) cattle to elucidate the extent of spread of ASIP-SV1 in different cattle populations. Of 216 sequences analyzed, 63 zebu (45.9%) and five taurine (6.3%) animals had at least one copy of ASIP-SV1. Four of the taurine animals presenting the SV were Romagnola cattle, a breed with history of zebu introgression. The remaining taurine animal was a Simmental, a breed frequently used in crossbreeding. These data provide evidence that ASIP-SV1 is commonly found in zebu populations, in addition to taurine animals with zebu admixture.