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Inflammation may adversely affect early human brain development. We aimed to assess the role of maternal nutrition and infections on cord blood inflammation. In a pregnancy cohort in Sylhet, Bangladesh, we enrolled 251 consecutive pregnancies resulting in a term livebirth from July 2016–March 2017. Stillbirths, preterm births, and cases of neonatal encephalopathy were excluded. We prospectively collected data on maternal diet (food frequency questionnaire) and morbidity, and analyzed umbilical cord blood for interleukin (IL)-1α, IL-1β, IL-6, IL-8 and C-reactive protein. We determined associations between nutrition and infection exposures and cord cytokine elevation (≥75% vs. <75%) using logistic regression, adjusting for confounders. One-third of mothers were underweight (BMI < 18.5 kg/m2) at enrollment. Antenatal and intrapartum infections were observed among 4.8% and 15.9% of the sample, respectively. Low pregnancy intakes of B vitamins (B1, B2, B3, B6, B9 (folate)), fat-soluble vitamins (D, E), iron, zinc, and linoleic acid (lowest vs. middle tertile) were associated with higher risk of inflammation, particularly IL-8. There was a non-significant trend of increased risk of IL-8 and IL-6 elevation with history of ante-and intrapartum infections, respectively. In Bangladesh, improving micronutrient intake and preventing pregnancy infections are targets to reduce fetal systemic inflammation and associated adverse neurodevelopmental outcomes. [ABSTRACT FROM AUTHOR]

Filter media (FM) sourced from recycled organic and mineral materials offer an effective and low cost means of treating urban stormwater. Using recycled materials rather than the increasingly scarce source of virgin materials (typically sandy loam soil) can ensure a sustainable and long-term economy and environment. This paper presents the results from the laboratory analysis and mathematical modelling to highlight the performance of recycled organic and mineral materials in removing nutrients and metals from stormwater. The analysis included the physical and chemical characterisation of particle size distribution, saturated hydraulic conductivity (Ksat), bulk density, effective cation exchange capacity, and pollutant removal performance. The design mixes (DM), comprising a combination of organic and mineral materials, were characterised and used to develop/derive the modelling design within the Model for Urban Stormwater Improvement Conceptualisation (MUSIC v6). Comparison is made to the Adoption Guidelines for Stormwater Biofiltration Systems—Summary Report which were based on the Facility for Advancing Water Biofiltration (FAWB) guidelines to assist in the development of biofiltration systems, including the planning, design, construction, and operation of those systems. An observed outcome from over two decades of biofiltration guideline development has been the exclusion of alternative biofilter materials due to claims of excessive leaching. Results from this study indicate that high nutrient and metal removal rates can be achieved over a range of hydraulic conductivities using design mixes of recycled organic and mineral materials that have a demonstrated equivalence to existing guideline specifications. [ABSTRACT FROM AUTHOR]

Recognizing the action of plastic bag taking from CCTV video footage represents a highly specialized and niche challenge within the broader domain of action video classification. To address this challenge, our paper introduces a novel benchmark video dataset specifically curated for the task of identifying the action of grabbing a plastic bag. Additionally, we propose and evaluate three distinct baseline approaches. The first approach employs a combination of handcrafted feature extraction techniques and a sequential classification model to analyze motion and object-related features. The second approach leverages a multiple-frame convolutional neural network (CNN) to exploit temporal and spatial patterns in the video data. The third approach explores a 3D CNN-based deep learning model, which is capable of processing video data as volumetric inputs. To assess the performance of these methods, we conduct a comprehensive comparative study, demonstrating the strengths and limitations of each approach within this specialized domain.

Today, huge amounts of time series data are sensed continuously by AIoT devices, transmitted to edge nodes, and to data centers. It costs a lot of energy to transmit these data, store them, and process them. Data compression technologies are commonly used to reduce the data size and thus save energy. When a certain level of data accuracy is sacrificed, lossy compression technologies can achieve better compression ratios. However, different applications may have different requirements for data accuracy. Instead of keeping multiple compressed versions of a time series w.r.t. different error bounds, HIRE hierarchically maintains a tree, where the root records a constant function to approximate the whole time series, and each other node records a constant function to approximate a part of the residual function of its parent for a particular time period. To retrieve data w.r.t. a specific error bound, it traverses the tree from the root down to certain levels according to the requested error bound and aggregates the constant functions on the visited nodes to generate a new bounded error compressed version dynamically. However, the number of nodes to be visited is unknown before the tree traversal completes, and thus the data size of the new version. In this paper, a time series is progressively decomposed into multiple piecewise linear functions. The first function is an approximation of the original time series w.r.t. the largest error bound. The second function is an approximation of the residual function between the original time series and the first function w.r.t. the second largest error bound, and so forth. The sum of the first, second, …, and m -th functions is an approximation of the original time series w.r.t. the m -th error bound. For each iteration, Swing-RR is used to generate a Bounded Error Piecewise Linear Approximation (BEPLA). Resolution Reduction (RR) plays an important role. Eight real-world datasets are used to evaluate the proposed method. For each dataset, approximations w.r.t. three typical error bounds, 5%, 1%, and 0.5%, are requested. Three BEPLAs are generated accordingly, which can be summed up to form three approximations w.r.t. the three error bounds. For all datasets, the total data size of the three BEPLAs is almost the same with the size used to store just one version w.r.t. the smallest error bound and significantly smaller than the size used to keep three independent versions. The experiment result shows that the proposed method, referred to as PBEPLA-RR, can achieve very good compression ratios and provide multiple approximations w.r.t. different error bounds.

Fumonisins, a class of mycotoxins predominantly produced by Fusarium species, represent a major threat to food safety and public health due to their widespread occurrence in staple crops including peanuts, wine, rice, sorghum, and mainly in maize and maize-based food and feed products. Although fumonisins occur in different groups, the fumonisin B series, particularly fumonisin B1 (FB1) and fumonisin B2 (FB2), are the most prevalent and toxic in this group of mycotoxins and are of public health significance due to the many debilitating human and animal diseases and mycotoxicosis they cause and their classification as by the International Agency for Research on Cancer (IARC) as a class 2B carcinogen (probable human carcinogen). This has made them one of the most regulated mycotoxins, with stringent regulatory limits on their levels in food and feeds destined for human and animal consumption, especially maize and maize-based products. Numerous countries have regulations on levels of fumonisins in foods and feeds that are intended to protect human and animal health. However, there are still gaps in knowledge, especially with regards to the molecular mechanisms underlying fumonisin-induced toxicity and their full impact on human health. Detection of fumonisins has been advanced through various methods, with immunological approaches such as Enzyme-Linked Immuno-Sorbent Assay (ELISA) and lateral flow immunoassays being widely used for their simplicity and adaptability. However, these methods face challenges such as cross-reactivity and matrix interference, necessitating the need for continued development of more sensitive and specific detection techniques. Chromatographic methods, including HPLC-FLD, are also employed in fumonisin analysis but require meticulous sample preparation and derivitization due to the low UV absorbance of fumonisins. This review provides a comprehensive overview of the fumonisin family, focusing on their biosynthesis, occurrence, toxicological effects, and levels of contamination found in foods and the factors affecting their presence. It also critically evaluates the current methods for fumonisin detection and quantification, including chromatographic techniques and immunological approaches such as ELISA and lateral flow immunoassays, highlighting the challenges associated with fumonisin detection in complex food matrices and emphasizing the need for more sensitive, rapid, and cost-effective detection methods.

Amyloidosis is a rare pathology characterized by protein deposits in various organs and tissues. Cardiac amyloidosis (CA) can be caused by various protein deposits, but transthyretin amyloidosis (ATTR) and immunoglobulin light chain (AL) are the most frequent pathologies. Protein misfolding can be induced by several factors such as oxidative stress, genetic mutations, aging, chronic inflammation, and neoplastic disorders. In ATTR cardiomyopathy (ATTR-CM), the amyloid fibrils can be found in the myocardium interstitial space and are associated with arrhythmias and heart failure. In pathological situations, the transthyretin (TTR) configuration is destroyed by proteolytic action, leading to monomers that further misfold and aggregate to form the amyloid fibrils. 99m Tc-Pyrophosphate ( 99m -Tc-PYP), 99m Tc 3,3-diphosphono-1,2-propanodicarboxylic acid ( 99m -Tc-DPD) and 99m -Tc hydroxy-methylene-Dyphosphonate ( 99m -Tc-HMDP) are used to detect myocardium amyloid deposits due to their ability to detect calcium ions that are present in the amyloid fibrils through dystrophic calcification. ATTR-CM therapy acts on different stages of the amyloidogenic process, including liver TTR synthesis, TTR tetramer destabilization, and misfolding of the monomers. The main aim of this narrative review is to present ATTR-CM, starting with molecular changes regarding the protein misfolding process and radionuclide aspects and finishing with pharmacological approaches.

Precision depth estimation plays a key role in many applications, including 3D scene reconstruction, virtual reality, autonomous driving and human-computer interaction. Through recent advancements in deep learning technologies, monocular depth estimation, with its simplicity, has surpassed the traditional stereo camera systems, bringing new possibilities in 3D sensing. In this paper, by using a single camera, we propose an end-to-end supervised monocular depth estimation autoencoder, which contains an encoder with a structure with a mixed convolution neural network and vision transformers and an effective adaptive fusion decoder to obtain high-precision depth maps. In the encoder, we construct a multi-scale feature extractor by mixing residual configurations of vision transformers to enhance both local and global information. In the adaptive fusion decoder, we introduce adaptive fusion modules to effectively merge the features of the encoder and the decoder together. Lastly, the model is trained using a loss function that aligns with human perception to enable it to focus on the depth values of foreground objects. The experimental results demonstrate the effective prediction of the depth map from a single-view color image by the proposed autoencoder, which increases the first accuracy rate about 28% and reduces the root mean square error about 27% compared to an existing method in the NYU dataset.

Background: Muscle depletion (MD) is a critical factor that influences clinical outcomes in patients with hepatocellular carcinoma (HCC). Although its role in cancer prognosis is recognized, its integration into widely used prognostic systems remains underexplored. This study aimed to evaluate the prognostic impact of MD on overall survival (OS) in HCC patients and to improve existing noninvasive prognostic models by incorporating MD-related metrics., Methods: A retrospective analysis was conducted on 1072 HCC patients treated at Taipei Tzu Chi Hospital between January 2006 and December 2016. All patients had follow-up data and computed tomography (CT) scans at vertebral level L3 for MD evaluation. Independent prognostic factors for OS were identified using Cox proportional hazards models, and the predictive performance of various prognostic models was assessed through the area under the receiver operating characteristic curve (AUROC)., Results: The key independent predictors of OS in HCC patients included hepatitis B virus infection, hepatitis C virus infection, liver cirrhosis, tumor size, serum alpha-fetoprotein levels, and MD-related metrics (psoas muscle-to-spine ratio, psoas muscle-to-vertebral ratio, and myosteatosis). Among existing models, the Barcelona Clinic Liver Cancer (BCLC) stage, the Child-Turcotte-Pugh (CTP) class, and the albumin-bilirubin (ALBI) grade demonstrated robust predictive performance for OS. However, incorporating MD significantly improved the predictive accuracy of these models, with the MD-BCLC model showing the highest AUROC (0.804, 95% CI: 0.777-0.832, p < 0.001)., Conclusions: MD is an independent and significant prognostic predictor for patients with HCC. Integrating MD metrics into established systems, particularly the BCLC staging system, markedly improves OS prediction, providing a more comprehensive tool for clinical decision-making in the management of HCC.

Citrullination, a post-translational modification (PTM), plays a critical role in rheumatoid arthritis (RA) by triggering immune responses to citrullinated self-antigens. Some HLA-DRB1 genes encode molecules with the shared epitope (QKRAA/QRRAA) sequence in the peptide-binding groove which preferentially presents citrulline-modified peptides, like vimentin, that intensifies the immune response in RA. In this study, we used computational approaches to evaluate intermolecular interactions between vimentin peptide-ligands (with/without PTM) and HLA-DRB1 alleles associated with a significantly increased risk for RA development. Crystal structures for HLA-DRB1*04:01, *04:04, and *04:05 bound to citrullinated peptides (PDB ID: 4MCY, 4MD5, 6BIR) were retrieved from the Protein Data Bank and non-citrullinated 3D structures were generated by mutating citrulline to arginine. The pHLA complexes were submitted to four rounds (50 ns each) of molecular dynamic simulations (MD) with Gromacs v.2022. Our results show that citrulline strengthens the interaction between vimentin and the HLA-DRB1 molecules, therefore impacting both the peptide affinity to the HLAs and pHLA stability; it also induces more intermolecular hydrogen bond formation during MD in the pHLA. Citrulline prevents repulsion between amino acid 71β and the P4-residue of native vimentin. Thus, vimentin citrullination seems to affect pHLA binding and dynamics, which may influence RA-related immune responses.

Background: The 5-year prognosis of non-high-risk neuroblastomas is generally good (>90%). However, a proportion of patients show progression and succumb to their disease. We aimed to identify molecular aberrations (not incorporated in the current risk stratification) associated with overall survival (OS) and/or event-free survival (EFS) in patients diagnosed with non-high-risk neuroblastoma., Methods: We conducted a systematic search in PubMed, Embase, Cochrane and Google Scholar. Two reviewers independently and blindly screened titles/abstracts, references of protocols/reviews and full texts. Risk of bias was assessed using a customized Quality in Prognostic Studies tool. Applicability was assessed using a tool designed by the researchers. GRADE criteria were used to determine quality of evidence., Results: Sixteen studies (4718 patients) were included. A segmental chromosomal aberration (SCA) profile was associated with lower survival. 1p loss of heterozygosity (LOH) and 17q gain were associated with lower OS and EFS. 1p deletion and 2p gain were associated with lower OS, but this was not the same for EFS. 3p deletion was not associated with worse outcome. Quality of evidence was downgraded because of imprecision and publication bias and upgraded because of moderate/large effect, resulting in a moderate quality of evidence., Conclusion: The association of 1p LOH, 1p deletion, 2p gain and 17q gain with OS and EFS suggests that these SCAs may be added to the risk stratification to identify non-high-risk neuroblastomas with worse prognosis.

The most commonly used methods to chemically assess grape and wine quality with high sensitivity and selectivity require lengthy analysis time and can be resource intensive. Here, we developed a rapid and non-destructive method that would help in grading and decision support. In this work, we demonstrate that integrating a three-dimensional (3D) material for volatile sampling with mass spectrometry detection can be used to sample grapes for phytosanitary, quality or smoke-taint assessments at low levels of marker compounds. An efficient zeolitic imidazolate framework-8 (ZIF-8) material was synthesised in situ on nickel foam (NF), taking advantage of its ultrahigh surface area, structural diversity, and functionality as an emerging nanostructured material for preconcentrating low-level wine and grape quality-related volatiles. When used as a sorbent in thermal desorption tubes and coupled directly to active capillary mass spectrometry, the average signal across the selected analytes increased by ~50% as compared to Tenax TA, a commercially available polymer, in a measurement that takes less than two minutes. The first integration of 3D materials into mass spectrometry opens new possibilities for developing new material architecture with enhanced selectivity of next-generation multifunctional instrumentation for volatile analysis and product quality assessment.

Tissue acidification resulting from dysregulated cellular bioenergetics accompanies various inflammatory states. GPR68, along with other members of proton-sensing G protein-coupled receptors, responds to extracellular acidification and has been implicated in chronic inflammation-related diseases such as ischemia, cancer, and colitis. The present study examined the role of extracellular acidification on human pulmonary endothelial cell (EC) permeability and inflammatory status per se and investigated potential synergistic effects of acidosis on endothelial dysfunction caused by bacterial lipopolysaccharide (LPS, Klebsiella pneumoniae ). Results showed that medium acidification to pH 6.5 caused a delayed increase in EC permeability illustrated by a decrease in transendothelial electrical resistance and loss of continuous VE-cadherin immunostaining at cell junctions. Likewise, acidic pH induced endothelial inflammation reflected by increased mRNA and protein expression of EC adhesion molecules VCAM-1 and ICAM-1, upregulated mRNA transcripts of tumor necrosis factor-α, IL-6, IL-8, IL-1β, and CXCL5, and increased secretion of ICAM-1, IL-6, and IL-8 in culture medium monitored by ELISA. Among the GPCRs tested, acidic pH selectively increased mRNA and protein expression of GPR68, and only the GPR68-specific small molecule inhibitor OGM-8345 rescued acidosis-induced endothelial permeability and inflammation. Furthermore, acidic pH exacerbated LPS-induced endothelial permeability and inflammatory response in cultured lung macrovascular as well as microvascular endothelial cells. These effects were suppressed by OGM-8345 in both EC types. Altogether, these results suggest that GPR68 is a critical mediator of acidic pH-induced dysfunction of human pulmonary vascular endothelial cells and mediates the augmenting effect of tissue acidification on LPS-induced endothelial cell injury.

Social housing provides a high level of enrichment for captive non-human primates, but providing this in research situations can be challenging. We have developed a multifactorial animal selection and introduction process coordinated by veterinary and animal care behavioral teams. This process sought to successfully establish lasting same-sex pairs and trios for African green monkeys ( Chlorocebus sabaeus ) in studies lasting from three months to over a year. The selection and introduction process addresses variables that impact a successful grouping, including age, animal weight difference, cage size and configuration. Evaluation and consideration of these initial matchmaking parameters has favorably enhanced pairing/trio bonding. Social housing with females has been typically more successful than with males, with 90-100% of females successfully pair- or trio-housed. Successful pairs and trios exhibit multiple affiliative behaviors and demonstrate better overall health while under study, ensuring benefits both to animal welfare and study data.

The prognostic value of easily accessible hematologic biomarkers, such as the neutrophil-to-HDL ratio, as well as the monocyte-to-HDL, lymphocyte-to-HDL, and platelet-to-HDL ratios, remains underexplored in patients with established ischemic heart disease (IHD). Community-dwelling adults aged ≥ 20 with established IHD from the National Health and Nutrition Examination Survey (NHANES) 1999-2018 were eligible. Mortality was tracked through linkage to the National Death Index (NDI) until the end of 2019. Cox regressions examined the associations between these hematologic ratios and all-cause mortality. Receiver operating characteristic (ROC) curve analysis assessed the predictive accuracy of these ratios for mortality. A total of 2265 patients were analyzed, with a median follow-up period of 80 months. After adjusting for demographic factors, lifestyle variables, and comorbidities, patients in the highest quartile of the neutrophil/HDL had a significantly increased all-cause mortality risk (aHR = 1.41, 95% CI: 1.13-1.77) compared to those in the lowest quartile. No significant associations were found between the other ratios and mortality. In conclusion, this study found that among the hematologic ratios analyzed, an elevated neutrophil-to-HDL ratio has the strongest potential for mortality risk stratification in community-dwelling patients with ischemic heart disease in the US, offering important guidance for both patients and clinicians., Competing Interests: The authors have indicated that they have no competing interests.

The association between psoriasis and increased cancer risk is gaining recognition as studies reveal shared inflammatory and immune pathways. This review examines the relationship between psoriasis and neoplasia, focusing on cancer risk factors in psoriasis patients, the biological pathways underlying this connection, and the impact of various psoriasis treatments on cancer development. Psoriasis patients have a heightened incidence of certain cancers, such as lymphomas, skin cancers, and urological malignancies, potentially linked to immune dysregulation and chronic inflammation. Immunomodulatory treatments for psoriasis, including conventional systemic therapies and biologics, present varied cancer risks, with others, such as phototherapy, associated with an elevated risk of skin cancers. For oncologic patients with psoriasis, management necessitates a tailored approach, balancing effective psoriasis control with minimizing cancer progression risks. The emergence of IL-17 inhibitors, IL-23 inhibitors, and small-molecule therapies offers promising therapeutic alternatives with favorable safety profiles for these patients. This review underscores the need for interdisciplinary collaboration to optimize care for patients managing both psoriasis and malignancy.

A fractal-order entropy dynamics model is developed to create a modified form of Maxwell's time-dependent electromagnetic equations. The approach uses an information-theoretic method by combining Shannon's entropy with fractional moment constraints in time and space. Optimization of the cost function leads to a time-dependent Bayesian posterior density that is used to homogenize the electromagnetic fields. Self-consistency between maximizing entropy, inference of Bayesian posterior densities, and a fractal-order version of Maxwell's equations are developed. We first give a set of relationships for fractal derivative definitions and their relationship to divergence, curl, and Laplacian operators. The fractal-order entropy dynamic framework is then introduced to infer the Bayesian posterior and its application to modeling homogenized electromagnetic fields in solids. The results provide a methodology to help understand complexity from limited electromagnetic data using maximum entropy by formulating a fractal form of Maxwell's electromagnetic equations.

In the waste oil recycling industry, large amounts of oil-containing sludge are still generated, thus posing a resource depletion issue when disposed of or incinerated without energy recovery or residual oil utilization. In this work, chemical activation experiments using phosphoric acid (H 3 PO 4 ) were performed at a low temperature (600 °C) for 30 min to produce porous carbon products. From the results of the pore property analysis, an increasing trend with an increasing impregnation ratio from 0.5 to 2.0 was observed. Based on the Brunauer-Emmett-Teller (BET) model, the maximal BET surface area was about 70 m 2 /g, which was indicative of the hysteresis loop and the type IV isotherms in the resulting carbon product. In addition, the enhancement in the pore properties of the carbon products obtained through acid-washing was superior to that achieved through water-washing and without post-washing. From observations made using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS), the carbon products featured a porous structure and inherent magnetism due to their richness of iron oxides. In this regard, they can be used as efficient adsorbents or catalyst supports due to their simple recovery (or separation) when exhausted.

This study evaluated the concentrations of tetracycline, erythromycin, and sulfonamide in beef, eggs, and honey products sold at grocery stores and whether standard cooking to 160 °F (71 °C) reduced the concentrations of antimicrobials spiked into the evaluated food products. In June 2021, the concentrations of selected antimicrobials in 65 samples (8 beef, 48 eggs, 9 honey samples) purchased from grocery stores within Knoxville, Tennessee, were measured using enzyme-linked immunosorbent assays. The Kruskal-Wallis test was used to assess the differences in the median tetracycline concentrations across food types, while the Mann-Whitney test was used to compare the erythromycin residue concentrations between beef and honey. Linear regression was used to analyze whether standard cooking at 160 °F (71 °C) reduced antimicrobial concentrations. Detectable tetracycline concentrations were found in all beef (8/8, 100%), eggs (46/48, 96%), and honey (9/9, 100%) samples, with median concentrations of 7.73, 5.62, and 13.13 µg/kg, respectively. Honey had significantly higher tetracycline concentrations than eggs ( p = 0.002). Detectable erythromycin concentrations were found in beef (5/8, 63%) and honey (9/9, 100%), with median concentrations of 0.14 µg/kg for beef and 0.48 µg/kg for honey. None of the antimicrobial concentrations found in the beef and egg products exceeded the U.S. FDA's maximum residue limits (MRL). Sulfonamide concentrations were undetectable in the beef and egg products. The concentrations of tetracycline, erythromycin, and sulfonamide spiked into the food products did not change significantly in response to cooking to 160 °F (71 °C). The antimicrobial concentrations found in the selected commercial food products were below the MRL, and cooking appeared to not reduce the spiked antimicrobial concentrations in the evaluated food products.

Local anesthetics are commonly used in various clinical settings for both prevention and symptom relief. Numerous clinical studies have demonstrated that intra-articular injections of local anesthetics achieve high success rates in orthopedic practices. However, several widely used local anesthetics, including bupivacaine, lidocaine, and ropivacaine, have been shown to exhibit toxicity to chondrocytes, with the underlying mechanisms of chondrotoxicity remaining poorly understood. In this study, we aimed to investigate the cytotoxic effects of local anesthetics, specifically focusing on the consequences of a single intra-articular injection in human chondrocyte cells. Our results reveal that lidocaine, levobupivacaine, bupivacaine, and ropivacaine induced cell death, characterized by the induction of apoptosis and the suppression of cellular proliferation. These effects were mediated through mechanisms involving oxidative stress, mitochondrial dysfunction, and autophagy pathways. We found that the toxic effects of local anesthetics were concentration-dependent, with lidocaine exhibiting the lowest cytotoxicity among the tested agents in TC28a cells. Notably, bupivacaine and levobupivacaine displayed significant cytotoxic effects related to apoptosis, cellular proliferation, reactive oxygen species generation, mitochondrial membrane potential depolarization, and autophagy in human chondrocyte cells. Our findings not only support existing clinical studies but also highlight potential targets for developing protective agents to mitigate serious side effects associated with their use in orthopedic practices.

A double-blind, randomized, and controlled clinical trial with therapeutic intervention was performed at a university hospital in Rio de Janeiro to evaluate whether the addition of Copaifera multijuga Hayne oleoresin to a carboxypolymethylene hydrogel is more effective in skin tear healing than standard treatment. The sample consisted of 84 patients, predominantly men, with a mean age of 67.37 years. These participants were divided into three groups (29 in the intervention group, which received 10% Copaifera oleoresin; 28 in the intervention group, which received 2% Copaifera oleoresin; and 27 in the control group, which received carboxypolymethylene hydrogel). Data were tabulated and analyzed according to the relevant protocols and included only patients who had completed the treatment, while losses were excluded. Weekly follow-ups were conducted to monitor progress. The average healing time differed among the three groups ( p > 0.05). There was also a significant difference in healing time between the two intervention groups. Ultimately, CopaibaPolyHy-2 led to significantly faster wound healing than CopaibaPolyHy-10 ( p < 0.05). A high increase in granulation and epithelial tissue and a decrease in exudate quantity were observed in the CopaibaPolyHy-2 group. It was not possible to infer whether the wound size reduction differed between the treatments ( p > 0.05). At the end of the study, 100% of wounds were healed, with 47,6% healing in week 2 (n = 40). No participants experienced local or serious adverse effects throughout the study period. The current study shows that CopaibaPolyHy-2 is effective, offering a statistically significantly faster healing time, better-quality tissue, and safe treatment for skin tears.

This study introduced a novel approach to 3D image segmentation utilizing a neural network framework applied to 2D depth map imagery, with Z axis values visualized through color gradation. This research involved comprehensive data collection from mechanically harvested wild blueberries to populate 3D and red-green-blue (RGB) images of filled totes through time-of-flight and RGB cameras, respectively. Advanced neural network models from the YOLOv8 and Detectron2 frameworks were assessed for their segmentation capabilities. Notably, the YOLOv8 models, particularly YOLOv8n-seg, demonstrated superior processing efficiency, with an average time of 18.10 ms, significantly faster than the Detectron2 models, which exceeded 57 ms, while maintaining high performance with a mean intersection over union (IoU) of 0.944 and a Matthew's correlation coefficient (MCC) of 0.957. A qualitative comparison of segmentation masks indicated that the YOLO models produced smoother and more accurate object boundaries, whereas Detectron2 showed jagged edges and under-segmentation. Statistical analyses, including ANOVA and Tukey's HSD test (α = 0.05), confirmed the superior segmentation performance of models on depth maps over RGB images ( p < 0.001). This study concludes by recommending the YOLOv8n-seg model for real-time 3D segmentation in precision agriculture, providing insights that can enhance volume estimation, yield prediction, and resource management practices.

Cognitive deficits are emerging as critical targets for managing schizophrenia and enhancing clinical and functional outcomes. These deficits are pervasive among individuals with schizophrenia, affecting various cognitive domains. Traditional pharmacotherapy and cognitive behavioral therapy (CBT) have limitations in effectively addressing cognitive impairments in this population. Neuromodulation techniques show promise in improving certain cognitive domains among patients with schizophrenia spectrum disorders. Understanding the mechanisms of neural circuits that underlie cognitive enhancement is essential for elucidating the pathophysiological processes of the disorder, and these insights could significantly optimize strategies for managing schizophrenia. Meanwhile, although there is an increasing body of evidence demonstrating the therapeutic effects of neuromodulation in this area, further research is still needed, particularly regarding topics such as different treatment protocols and the long-term effects of treatment.

Wearable accelerometers are widely used as an ecologically valid and scalable solution for long-term at-home sleep monitoring in both clinical research and care. In this study, we applied a deep learning domain adversarial convolutional neural network (DACNN) model to this task and demonstrated that this new model outperformed existing sleep algorithms in classifying sleep-wake and estimating sleep outcomes based on wrist-worn accelerometry. This model generalized well to another dataset based on different wearable devices and activity counts, achieving an accuracy of 80.1% (sensitivity 84% and specificity 58%). Compared to commonly used sleep algorithms, this model resulted in the smallest error in wake after sleep onset (MAE of 48.7, Cole-Kripke of 86.2, Sadeh of 108.2, z-angle of 57.5) and sleep efficiency (MAE of 11.8, Cole-Kripke of 18.4, Sadeh of 23.3, z-angle of 9.3) outcomes. Despite being around for many years, accelerometer-alone devices continue to be useful due to their low cost, long battery life, and ease of use. Improving the accuracy and generalizability of sleep algorithms for accelerometer wrist devices is of utmost importance. We here demonstrated that domain adversarial convolutional neural networks can improve the overall accuracy, especially the specificity, of sleep-wake classification using wrist-worn accelerometer data, substantiating its use as a scalable and valid approach for sleep outcome assessment in real life.

Background/Objectives : This study evaluates the effect of Mn(II) and Co(II) ions on the production of anti- Candida metabolites by the endophytic fungus Aspergillus sp., isolated from Dizygostemon riparius . The objective was to identify metal-induced secondary metabolites with antifungal potential against drug-resistant Candida species. Methods : Aspergillus sp. was cultivated in Czapek agar supplemented with MnCl₂ (400 µM) or CoCl₂ (200 µM). Metabolite profiles were analyzed using UHPLC-DAD and LC-ESI-HRMS, followed by structural elucidation via NMR. Antifungal and biofilm inhibition activities were tested against Candida albicans and Candida parapsilosis . Toxicity was assessed using Tenebrio molitor larvae. Results : Key metabolites, including pyrophen, penicillquei B, and fonsecinone B, demonstrated antifungal activity with MIC values of 4.37-280.61 µg/mL. Fonsecinone B exhibited superior biofilm inhibition, surpassing fluconazole in reducing biofilm biomass and viability. In vivo assays showed low toxicity, with survival rates above 80% at 2× MIC/kg. Conclusions : Mn(II) and Co(II) significantly modulated the production of antifungal metabolites in Aspergillus sp. Fonsecinone B emerged as a promising candidate for antifungal therapy due to its potent activity and low toxicity. These findings support further investigation into the therapeutic potential of metal-induced fungal metabolites for combating drug-resistant Candida infections.