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Background: A valid malnutrition screening tool (MST) is essential to provide timely nutrition support in ambulatory cancer care settings. The aim of this study is to investigate the validity of the Patient-Generated Subjective Global Assessment Short Form (PG-SGA SF) and the new Global Leadership Initiative on Malnutrition (GLIM) criteria as compared to the reference standard, the Patient-Generated Subjective Global Assessment (PG-SGA). Methods: Cross-sectional observational study including 246 adult ambulatory patients with cancer receiving in-chair intravenous treatment at a cancer care centre in Australia. Anthropometrics, handgrip strength and patient descriptive data were assessed. Nutritional risk was identified using MST and PG-SGA SF, nutritional status using PG-SGA and GLIM. Sensitivity (Se), specificity (Sp), positive and negative predictive values and kappa (k) were analysed. Associations between malnutrition and 1-year mortality were investigated by Cox survival analyses. Results: A PG-SGA SF cut-off score ≥5 had the highest agreement when compared with the PG-SGA (Se: 89%, Sp: 80%, k = 0.49, moderate agreement). Malnutrition risk (PG-SGA SF ≥ 5) was 31% vs. 24% (MST). For malnutrition according to GLIM, the Se was 76% and Sp was 73% (k = 0.32, fair agreement) when compared to PG-SGA. The addition of handgrip strength to PG-SGA SF or GLIM did not improve Se, Sp or agreement. Of 100 patients who provided feedback, 97% of patients found the PG-SGA SF questions easy to understand, and 81% reported that it did not take too long to complete. PG-SGA SF ≥ 5 and severe malnutrition by GLIM were associated with 1-year mortality risk. Conclusions: The PG-SGA SF and GLIM criteria are accurate, sensitive and specific malnutrition screening and assessment tools in the ambulatory cancer care setting. The addition of handgrip strength tests did not improve the recognition of malnutrition or mortality risk.

The electrical activity of the diaphragm (Edi) is considered a new respiratory vital sign for monitoring breathing patterns and efforts during ventilator care. However, the Edi signal contains irregular noise from complex causes, which makes reliable breathing analysis difficult. Deep learning was implemented to accurately detect the Edi signal peaks and analyze actual neural breathing in premature infants. Edi signals were collected from 17 premature infants born before gestational age less than 32 weeks, who received ventilatory support with a non-invasive neurally adjusted ventilatory assist. First, a local maximal detection method that over-detects candidate Edi peaks was used. Subsequently, a convolutional neural network-based deep learning was implemented to classify candidates into final Edi peaks. Our approach showed superior performance in all aspects of respiratory Edi peak detection and neural breathing analysis compared with the currently used recording technique in the ventilator. The method obtained a f1-score of 0.956 for the Edi peak detection performance and R 2 value of 0.823 for respiratory rates based on the number of Edi peaks. The proposed technique can achieve a more reliable analysis of Edi signals, including evaluation of the respiration rate in premature infants.

Brown patch caused by the basidiomycete fungus Rhizoctonia solani is an economically important disease of cool-season turfgrasses. In order to manage the disease, different types of fungicides have been applied, but the negative impact of fungicides on the environment continues to rise. In this study, the beneficial bacteria Bacillus velezensis GH1-13 was characterized as a potential biocontrol agent to manage brown patch disease. The strain GH1-13 strongly inhibited the mycelial growth of turf pathogens including different anastomosis groups of R. solani causing brown patch and large patch. R. solani AG2-2(IIIB) hyphae were morphologically changed, and fungal cell death resulted from exposure to the strain GH1-13. In addition, the compatibility of fungicides with the bacterial strain, and the combined application of fungicide azoxystrobin and the strain in brown patch control on creeping bentgrass indicated that the strain could serve as a biocontrol agent. To develop strain-specific detection method, two unique genes from chromosome and plasmid of GH1-13 were found using pan-genome analysis of 364 Bacillus strains. The unique gene from chromosome was successfully detected using both SYBR Green and TaqMan qPCR methods in bacterial DNA or soil DNA samples. This study suggests that application of GH1-13 offers an environmentally friendly approach via reducing fungicide application rates. Furthermore, the developed pipeline of strain-specific detection method could be a useful tool for detecting and studying the dynamics of specific biocontrol agents.

The fundamental characteristics of collective interactions in topological band structures can be revealed by the exploration of charge screening in topological materials. In particular, distinct anisotropic screening behaviors are predicted to occur in Dirac nodal line semimetals (DNLSMs) due to their peculiar anisotropic low‐energy dispersion. Despite the recent extensive theoretical research, experimental observations of exotic charge screening in DNLSMs remain elusive, which is partly attributed to the coexisting trivial bands near the Fermi energy. This study reports the first direct observation of highly anisotropic charge‐screening behavior in the DNLSM SrAs3. Through atomically resolved conductance measurements, a highly anisotropic charge‐screening pattern around charged impurities on a surface is demonstrated. Moreover, the combination of model studies and first‐principles calculations reveals the unique nature of the screening anisotropy in DNLSMs. The results of this study are expected to pave the way for understanding the profound collective behavior of interacting low‐energy fermions in topological materials. This research presents the first experimental evidence of unique charge screening in a Dirac nodal line semimetal. Using scanning tunneling microscopy and spectroscopy, the distinct direction‐dependent and concave‐shaped charge screening pattern around charged impurities on the surface of SrAs3 is directly observed. The unique nature of this anisotropic screening is revealed by combining these measurements with theoretical models and calculations.

Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug–target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI’s expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI’s growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.

BACE1 is the rate-limiting enzyme for amyloid-β peptides (Aβ) generation, a key event in the pathogenesis of Alzheimer’s disease (AD). By an unknown mechanism, levels of BACE1 and a BACE1 mRNA-stabilizing antisense RNA (BACE1-AS) are elevated in the brains of AD patients, implicating that dysregulation of BACE1 expression plays an important role in AD pathogenesis. We found that nuclear factor erythroid-derived 2-related factor 2 (NRF2/NFE2L2) represses the expression of BACE1 and BACE1-AS through binding to antioxidant response elements (AREs) in their promoters of mouse and human. NRF2-mediated inhibition of BACE1 and BACE1-AS expression is independent of redox regulation. NRF2 activation decreases production of BACE1 and BACE1-AS transcripts and Aβ production and ameliorates cognitive deficits in animal models of AD. Depletion of NRF2 increases BACE1 and BACE1-AS expression and Aβ production and worsens cognitive deficits. Our findings suggest that activation of NRF2 can prevent a key early pathogenic process in AD.

Bacterial cellulose nanofiber (BCNF) with high thermal stability produced by an ecofriendly process has emerged as a promising solution to realize safe and sustainable materials in the large-scale battery. However, an understanding of the actual thermal behavior of the BCNF in the full-cell battery has been lacking, and the yield is still limited for commercialization. Here, we report the entire process of BCNF production and battery manufacture. We systematically constructed a strain with the highest yield (31.5%) by increasing metabolic flux and improved safety by introducing a Lewis base to overcome thermochemical degradation in the battery. This report will open ways of exploiting the BCNF as a “single-layer” separator, a good alternative to the existing chemical-derived one, and thus can greatly contribute to solving the environmental and safety issues.

Background While there is a high burden of methicillin-resistant Staphylococcus aureus (MRSA) infections among pediatric patients, studies on the molecular epidemiology of MRSA infections in Korean children since the 2010s are lacking. This study aimed to investigate the molecular genotypes and clinical characteristics of MRSA isolates from children with MRSA bacteremia at Asan Medical Center Children’s Hospital from 2016 to 2021. Methods Clinical data were retrospectively reviewed, and the molecular types of MRSA were determined using multilocus sequence typing (MLST) and Staphylococcal cassette chromosome mec (SCCmec) typing. Results The overall methicillin resistance rate of S. aureus bacteremia was 44.8% (77/172); 49.5% in the period 2016–2018 (period 1) and 37.3% in the period 2019–2021 (period 2) ( P  = 0.116). Community-acquired infections accounted for only 3.9% of cases. The predominant ST group was ST72 group (67.6%), followed by ST5 group (18.9%) and ST1 group (5.4%). The proportion of ST5 was significantly lower in period 2 compared to period 1 ( P  = 0.02). Compared to the ST5 and ST1 groups, the ST72 group exhibited lower overall antibiotic resistance and multidrug-resistant (MDR) rates (12.0% [6/50] in ST72 group vs. 100.0% [14/14] in ST5 group vs. 50.0% [2/4] in ST1 group; P  < 0.001). In the multivariate analysis, the ST1 group was an independent risk factor for 30-day all-cause mortality (aOR, 44.12; 95% CI, 3.46–562.19). Conclusion The ST72-MRSA strain remained the most frequently isolated genotype in Korean children, while the ST1 group emerged as an independent risk factor for 30-day all-cause mortality in pediatric MRSA bacteremia. Ongoing efforts to uncover the evolving epidemiology of MRSA are essential for developing effective strategies for prevention and treatment.

p21-activated kinase 4 (PAK4), a member of the PAK family (PAK1–6), was initially recognized for its role in tumor development. Recently, we discovered PAK4’s involvement in triacylglycerol lipolysis in adipocytes. However, its function in adipogenesis remains unclear. Here we show that PAK4 plays a critical role in adipocyte differentiation. Following adipogenic stimulation, PAK4 protein levels increased. Knockdown of PAK4 in 3T3-L1 preadipocytes or human stromal vascular cells, as well as pharmacological inhibition of PAK4 in 3T3-L1 cells, impaired adipogenesis, as indicated by reduced expression of adipocyte marker genes and decreased lipid accumulation. Mechanistically, PAK4 phosphorylated cyclin-dependent kinase 2 at serine 106, a critical step for CCAAT/enhancer-binding protein β expression during mitotic clonal expansion. Consistent with these findings, preadipocyte-specific Pak4 -knockout mice exhibited reduced fat mass and smaller adipocytes. These results reveal PAK4 as a crucial regulator of adipogenesis and, together with its inhibitory role in triacylglycerol lipolysis, further underscore its potential as a therapeutic target for obesity treatment. PAK4 regulates adipogenesis in fat cells Adipogenesis is the process by which stem cells become fat cells, and it is crucial for understanding obesity. Researchers identified a gap in understanding how the protein PAK4 affects this process. The study explored PAK4’s role in fat cell development and used human fat samples and mice to study this. They generated mice with specific genes turned off to examine the impact of PAK4 loss on fat development. They also used special techniques to look at proteins and their interactions. Researchers discovered that PAK4 helps another protein, CDK2, work better by adding a phosphate group to it. When they blocked PAK4, fat cell formation was reduced. This suggests that PAK4 is crucial for turning stem cells into fat cells. The study concludes that targeting PAK4 could be a new way to treat obesity by controlling how fat cells form. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

Background Accurate classification of breast cancer molecular subtypes is crucial in determining treatment strategies and predicting clinical outcomes. This classification largely depends on the assessment of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) status. However, variability in interpretation among pathologists pose challenges to the accuracy of this classification. This study evaluates the role of artificial intelligence (AI) in enhancing the consistency of these evaluations. Methods AI-powered HER2 and ER/PR analyzers, consisting of cell and tissue models, were developed using 1,259 HER2, 744 ER, and 466 PR-stained immunohistochemistry (IHC) whole-slide images of breast cancer. External validation cohort comprising HER2, ER, and PR IHCs of 201 breast cancer cases were analyzed with these AI-powered analyzers. Three board-certified pathologists independently assessed these cases without AI annotation. Then, cases with differing interpretations between pathologists and the AI analyzer were revisited with AI assistance, focusing on evaluating the influence of AI assistance on the concordance among pathologists during the revised evaluation compared to the initial assessment. Results Reevaluation was required in 61 (30.3%), 42 (20.9%), and 80 (39.8%) of HER2, in 15 (7.5%), 17 (8.5%), and 11 (5.5%) of ER, and in 26 (12.9%), 24 (11.9%), and 28 (13.9%) of PR evaluations by the pathologists, respectively. Compared to initial interpretations, the assistance of AI led to a notable increase in the agreement among three pathologists on the status of HER2 (from 49.3 to 74.1%, p  < 0.001), ER (from 93.0 to 96.5%, p  = 0.096), and PR (from 84.6 to 91.5%, p  = 0.006). This improvement was especially evident in cases of HER2 2+ and 1+, where the concordance significantly increased from 46.2 to 68.4% and from 26.5 to 70.7%, respectively. Consequently, a refinement in the classification of breast cancer molecular subtypes (from 58.2 to 78.6%, p  < 0.001) was achieved with AI assistance. Conclusions This study underscores the significant role of AI analyzers in improving pathologists' concordance in the classification of breast cancer molecular subtypes.