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Abstract Background Macrophages are highly plastic innate immune cells that play key roles in host defense, tissue repair, and homeostasis maintenance. In response to divergent stimuli, macrophages rapidly alter their functions and manifest a wide polarization spectrum with two extremes: M1 or classical activation and M2 or alternative activation. Extracellular vesicles (EVs) secreted from differentially activated macrophages have been shown to have diverse functions, which are primarily attributed to their microRNA cargos. The role of protein cargos in these EVs remains largely unexplored. Therefore, in this study, we focused on the protein cargos in macrophage-derived EVs. Results Naïve murine bone marrow-derived macrophages were treated with lipopolysaccharide or interlukin-4 to induce M1 or M2 macrophages, respectively. The proteins of EVs and their parental macrophages were subjected to quantitative proteomics analyses, followed by bioinformatic analyses. The enriched proteins of M1-EVs were involved in proinflammatory pathways and those of M2-EVs were associated with immunomodulation and tissue remodeling. The signature proteins of EVs shared a limited subset of the proteins of their respective progenitor macrophages, but they covered many of the typical pathways and functions of their parental cells, suggesting their respective M1-like and M2-like phenotypes and functions. Experimental examination validated that protein cargos in M1- or M2-EVs induced M1 or M2 polarization, respectively. More importantly, proteins in M1-EVs promoted viability, proliferation, and activation of T lymphocytes, whereas proteins in M2-EVs potently protected the tight junction structure and barrier integrity of epithelial cells from disruption. Intravenous administration of M2-EVs in colitis mice led to their accumulation in the colon, alleviation of colonic inflammation, promotion of M2 macrophage polarization, and improvement of gut barrier functions. Protein cargos in M2-EVs played a key role in their protective function in colitis. Conclusion This study has yielded a comprehensive unbiased dataset of protein cargos in macrophage-derived EVs, provided a systemic view of their potential functions, and highlighted the important engagement of protein cargos in the pathophysiological functions of these EVs.

The renal system is one of the major organ systems present in the human body, it is responsible for maintaining electrolyte balance, secreting important hormones, and maintaining homeostasis in the body. Every individual has 2 kidneys which are located behind the peritoneum at the level of L2-L3 from the vertebral column. Broadly kidneys function to filter blood, purify it, and eliminate toxic waste. We propose a diagnostic test that evaluates the renal blood flow rate in the kidneys and assesses their functioning based on their hemodynamics in the given time frame. Here we aim to explore the application of nanoparticles to assess localized blood pressure in the kidney based on the illumination intensity/radioactivity. Connexin40(Cx40) is a transmembrane protein present in juxtaglomerular apparatus, Cx40 gold antibodies prepared can be conjugated with nanoparticles and introduced in renal circulation. Imaging technologies such as fluorescence/computed tomography can be used to detect these nanoparticles. Once injected they can be assessed and information on renal blood flow can be obtained. The intensity generated on screening can be used for the assessment of renal blood pressure. This information will be valuable for the assessment of kidney function.

Abstract Objective Inhalational burns frequently lead to dysphonia and airway stenosis. We hypothesize local dexamethasone delivery via a novel drug‐eluting electrospun polymer‐mesh endotracheal tube (ETT) reduces biomechanical and histologic changes in the vocal folds in inhalational burn. Methods Dexamethasone‐loaded polymer mesh was electrospun onto ETTs trimmed to transglottic endolaryngeal segments and secured in nine Yorkshire Crossbreed swine with directed 150°C inhalation burns. Uncoated ETTs were implanted in nine additional swine with identical burns. ETT segments were maintained for 3 and 7 days. Vocal fold (VF) structural stiffness was measured using automated‐indentation mapping and compared across groups and to four uninjured controls, and matched histologic assessment performed. Statistical analysis was conducted using two‐way ANOVA with Tukey's post hoc test and Wilcoxon rank‐sum test. Results VF stiffness after burn decreased with longer intubation, from 19.4 (7.6) mN/mm at 3 days to 11.3 (5.2) mN/mm at 7 days (p

Abstract Background Female breast cancer remains the second leading cause of cancer-related death in the USA. The heterogeneity in the tumor morphology across the cohort and within patients can lead to unpredictable therapy resistance, metastasis, and clinical outcome. Hence, supplementing classic pathological markers with intrinsic tumor molecular markers can help identify novel molecular subtypes and the discovery of actionable biomarkers. Methods We conducted a large multi-institutional genomic analysis of paired normal and tumor samples from breast cancer patients to profile the complex genomic architecture of breast tumors. Long-term patient follow-up, therapeutic regimens, and treatment response for this cohort are documented using the Breast Cancer Collaborative Registry. The majority of the patients in this study were at tumor stage 1 (51.4%) and stage 2 (36.3%) at the time of diagnosis. Whole-exome sequencing data from 554 patients were used for mutational profiling and identifying cancer drivers. Results We identified 54 tumors having at least 1000 mutations and 185 tumors with less than 100 mutations. Tumor mutational burden varied across the classified subtypes, and the top ten mutated genes include MUC4, MUC16, PIK3CA, TTN, TP53, NBPF10, NBPF1, CDC27, AHNAK2, and MUC2. Patients were classified based on seven biological and tumor-specific parameters, including grade, stage, hormone receptor status, histological subtype, Ki67 expression, lymph node status, race, and mutational profiles compared across different subtypes. Mutual exclusion of mutations in PIK3CA and TP53 was pronounced across different tumor grades. Cancer drivers specific to each subtype include TP53, PIK3CA, CDC27, CDH1, STK39, CBFB, MAP3K1, and GATA3, and mutations associated with patient survival were identified in our cohort. Conclusions This extensive study has revealed tumor burden, driver genes, co-occurrence, mutual exclusivity, and survival effects of mutations on a US Midwestern breast cancer cohort, paving the way for developing personalized therapeutic strategies.

Background/objectiveSmoking prevalence in patients with chronic pancreatitis [CP] is high. We aimed to understand lifetime history of smoking and cohort trends in CP patients to inform effective strategies for smoking cessation.MethodData on 317 CP patients from the North American Pancreatitis Study 2 [NAPS2] Continuation and Validation Study and the NAPS2 Ancillary Study were analyzed. Smoking history was assessed for each phase of life from the onset of smoking to study enrollment. Data on second-hand smoke and drinking history were also collected. We compared demographic factors, drinking history, pain level and pancreas morphology by smoking status at age 25 (non-smoking,

Abstract The risk of exposure of the general public or military personnel to high levels of ionizing radiation from nuclear weapons or radiological accidents is a dire national security matter. The development of advanced molecular biodosimetry methods, those that measure biological response, such as transcriptomics, to screen large populations of radiation-exposed victims is key to improving survival outcomes during radiological mass casualty scenarios. In this study, nonhuman primates were exposed to either 12.0 Gy cobalt-60 gamma (total-body irradiation, TBI) or X-ray (partial-body irradiation, PBI) 24 h after administration of a potential radiation medical countermeasure, gamma-tocotrienol (GT3). Changes in the jejunal transcriptomic profiles in GT3-treated and irradiated animals were compared to healthy controls to assess the extent of radiation damage. No major effect of GT3 on radiation-induced transcriptome at this radiation dose was identified. About 80% of the pathways with a known activation or repression state were commonly observed between both exposures. Several common pathways activated due to irradiation include FAK signaling, CREB signaling in the neurons, phagosome formation, and G-protein coupled signaling pathway. Sex-specific differences associated with excessive mortality among irradiated females were identified in this study, including Estrogen receptor signaling. Differential pathway activation was also identified across PBI and TBI, pointing towards altered molecular response for different degrees of bone marrow sparing and radiation doses. This study provides insight into radiation-induced changes in jejunal transcriptional profiles, supporting the investigation for the identification of biomarkers for radiation injury and countermeasure efficacy.

Abstract Endoplasmic reticulum (ER) stress and mitochondrial dysfunction are found in lesions of multiple sclerosis (MS) and animal models of MS such as experimental autoimmune encephalomyelitis (EAE), and may contribute to the neuronal loss that underlies permanent impairment. We investigated whether glatiramer acetate (GA) can reduce these changes in the spinal cords of chronic EAE mice by using routine histology, immunostaining, and electron microscopy. EAE spinal cord tissue exhibited increased inflammation, demyelination, mitochondrial dysfunction, ER stress, downregulation of NAD+ dependent pathways, and increased neuronal death. GA reversed these pathological changes, suggesting that immunomodulating therapy can indirectly induce neuroprotective effects in the CNS by mediating ER stress.

The premise for this study emanated from the need to understand SARS-CoV-2 infections at the molecular level and to develop predictive tools for managing COVID-19 severity. With the varied clinical outcomes observed among infected individuals, creating a reliable machine learning (ML) model for predicting the severity of COVID-19 became paramount. Despite the availability of large-scale genomic and clinical data, previous studies have not effectively utilized multi-modality data for disease severity prediction using data-driven approaches. Our primary goal is to predict COVID-19 severity using a machine-learning model trained on a combination of patients’ gene expression, clinical features, and co-morbidity data. Employing various ML algorithms, including Logistic Regression (LR), XGBoost (XG), Naïve Bayes (NB), and Support Vector Machine (SVM), alongside feature selection methods, we sought to identify the best-performing model for disease severity prediction. The results highlighted XG as the superior classifier, with 95% accuracy and a 0.99 AUC (Area Under the Curve), for distinguishing severity groups. Additionally, the SHAP analysis revealed vital features contributing to prediction, including several genes such as COX14, LAMB2, DOLK, SDCBP2, RHBDL1, and IER3-AS1. Notably, two clinical features, the absolute neutrophil count and Viremia Categories, emerged as top contributors. Integrating multiple data modalities has significantly improved the accuracy of disease severity prediction compared to using any single modality. The identified features could serve as biomarkers for COVID-19 prognosis and patient care, allowing clinicians to optimize treatment strategies and refine clinical decision-making processes for enhanced patient outcomes.

For sustainability and efficiency in maintaining high crop yield and less chemically polluted agricultural lands, precise weed mapping is essential for the total implementation of site-specific weed management which currently stands as a major challenge in present day agriculture. In this research, the robustness of the training epochs of You Only Look Once (YOLO) v5s, a Convolutional Neural Network (CNN) model was evaluated for the development of an automatic crop and weeds classification using UAV images. The images were annotated using a bounding box and they were trained on google colaboratory over 100, 300, 500, 600, 700 and 1000 epochs. The model detected and categorized five different classes which are sugarcane (Saccharum officinarum), banana trees (Musa), spinach (Spinacia oleracea), pepper (Capsicum), and weeds. To find the optimal performance on the test set, the model was trained across several epochs, and training was stopped when the test performance (classification accuracy, precision, and recall) began to drop. The obtained result shows that the performance of the classifier improved significantly as the range of training epochs tends to rise from 100 through to 600 epochs. Meanwhile, a slight decline was observed as the number of epoch was increased to 700 when the classification accuracy, the precision of weed and recall of 65, 43 and 43%, respectively, was recorded as against 67, 78 and 34% that was obtained as the classification accuracy, weed precision and recall, respectively, at 600 epochs. This decline continued even when the epoch was increased to 1000 where classification accuracy, weed precision and recall of 65%, 45% and 40%, respectively was obtained. The results showed that the training epoch of YOLOv5s significantly affects the model's robustness in automatic crop and weep classification and identified 600 as the epoch for optimal performance.

Mosquitoes pose widespread threats to humans and other animals as disease vectors [1]. Day- versus night-biting mosquitoes occupy distinct time-of-day niches [2, 3]. Here, we explore day- versus night-biting female and male mosquitoes' innate temporal attraction/avoidance behavioral responses to light and their regulation by circadian circuit and molecular mechanisms. Day-biting mosquitoes Aedes aegypti, particularly females, are attracted to light during the day regardless of spectra. In contrast, night-biting mosquitoes, Anopheles coluzzii, specifically avoid ultraviolet (UV) and blue light during the day. Behavioral attraction to/avoidance of light in both species change with time of day and show distinct sex and circadian neural circuit differences. Males of both diurnal and nocturnal mosquito species show reduced UV light avoidance in anticipation of evening onset relative to females. The circadian neural circuits of diurnal/day- and nocturnal/night-biting mosquitoes based on PERIOD (PER) and pigment-dispersing factor (PDF) expression show similar but distinct circuit organizations between species. The basis of diurnal versus nocturnal behaviors is driven by molecular clock timing, which cycles in anti-phase between day- versus night-biting mosquitoes. Observed differences at the neural circuit and protein levels provide insight into the fundamental basis underlying diurnality versus nocturnality. Molecular disruption of the circadian clock severely interferes with light-evoked attraction/avoidance behaviors in mosquitoes. In summary, attraction/avoidance behaviors show marked differences between day- versus night-biting mosquitoes, but both classes of mosquitoes are circadian and light regulated, which may be applied toward species-specific control of harmful mosquitoes.

Abstract Lithium-ion batteries based on high-voltage cathode materials, such as LiCoPO4, despite being promising in terms of specific power, still suffer from poor cycle life due to the lower stability of common non-aqueous electrolytes at higher voltages. One way to overcome this issue might be decreasing the working potential of the battery by doping LiCoPO4 by Fe, thus reducing electrolyte degradation upon cycling. However, such modification requires a deep understanding of the structural behavior of cathode material upon lithiation/delithiation. Here we used a combination of operando synchrotron-based XRD and XAS to investigate the dynamics of d-metal local atomic structure and charge state upon cycling of LiCo0.5Fe0.5PO4 mixed d-metal olivine cathode material. Principal components analysis (PCA) of XAS data allowed the extraction of spectra of individual phases in the material and their concentrations. For both Co and Fe two components were extracted, they correspond to fully lithiated and delithiated phases of LixMPO4 (where M = Fe, Co). Thus, we were able to track the phase transitions in the material upon charge and discharge and quantitatively analyze the M2+/M3+ electrochemical conversion rate for both Fe and Co. Rietveld's refinement of XRD data allowed us to analyze the changes in the lattice of cathode material and their reversibility upon (de)lithiation during cycling. The calculation of DFT and Bader charge analysis expects the oxygen redox procedure combined with d-metals redox, which supplements iron charge variations and dominates at high voltages when x