Explore the vast range of titles available.

Chronic non-healing wounds have become a major worldwide healthcare burden. The impact of biofilms on chronic wound infection is well established. Despite increasing understanding of the underlying mechanism of biofilm formation in chronic wounds, current strategies for biofilm diagnosis in chronic wounds are still far from ideal. In this review, we briefly summarize the mechanism of biofilm formation and focus on current diagnostic approaches of chronic wound biofilms based on morphology, microbiology, and molecular assays. Innovative biotechnological approaches, such as wound blotting and transcriptomic analysis, may further shed light on this unmet clinical need. The continuous development of these sophisticated diagnostic approaches can markedly contribute to the future implementation of point-of-care biofilm detection in chronic wound care. The impact of biofilms on delayed wound healing has drawn increasing attention. Their importance led to the establishment of biofilm-based wound care where chronic wounds are treated using multipronged strategies to remove biofilms over wound beds to facilitate the recovery of epithelial integrity. Current clinical and preclinical diagnostic techniques fail to accurately identify pathogens and the precise location of biofilms over wound surfaces, rendering timely medical or surgical intervention to eradicate biofilms elusive. Wound blotting is a novel biotechnology that predicts wound outcomes and localizes biofilms on wound surfaces by determining the distribution pattern of tumor necrosis factor-alpha (TNF-α) and biofilm mucopolysaccharides. The rapid and objective analysis offered by this technique may assist clinicians in treating chronic wound biofilms.

Proteomics plays a vital role in biomedical research in the post-genomic era. With the technological revolution and emerging computational and statistic models, proteomic methodology has evolved rapidly in the past decade and shed light on solving complicated biomedical problems. Here, we summarize scientific research and clinical practice of existing and emerging high-throughput proteomics approaches, including mass spectrometry, protein pathway array, next-generation tissue microarrays, single-cell proteomics, single-molecule proteomics, Luminex, Simoa and Olink Proteomics. We also discuss important computational methods and statistical algorithms that can maximize the mining of proteomic data with clinical and/or other ‘omics data. Various principles and precautions are provided for better utilization of these tools. In summary, the advances in high-throughput proteomics will not only help better understand the molecular mechanisms of pathogenesis, but also to identify the signature signaling networks of specific diseases. Thus, modern proteomics have a range of potential applications in basic research, prognostic oncology, precision medicine, and drug discovery.

Oxidative stress is an important pathomechanism found in numerous ocular degenerative diseases. To provide a better understanding of the mechanism and treatment of oxidant/antioxidant imbalance-induced ocular diseases, this article summarizes and provides updates on the relevant research. We review the oxidative damage (e.g., lipid peroxidation, DNA lesions, autophagy, and apoptosis) that occurs in different areas of the eye (e.g., cornea, anterior chamber, lens, retina, and optic nerve). We then introduce the antioxidant mechanisms present in the eye, as well as the ocular diseases that occur as a result of antioxidant imbalances (e.g., keratoconus, cataracts, age-related macular degeneration, and glaucoma), the relevant antioxidant biomarkers, and the potential of predictive diagnostics. Finally, we discuss natural antioxidant therapies for oxidative stress-related ocular diseases.

In the digital economy era, small service business struggle to compete in a rapidly changing and dynamic market. Therefore, through digital transformation (DT), small service business seek to increase their competitive advantage, improve business performance, and achieve business growth. Having limited resources and capabilities, small service business must deal with several barriers and challenges. This study aims to discover the barriers, and the roles of government, for digital transformation in small service business. This study applied a qualitative approach involving semi-structured in-depth interviews with top management of small service business. Then, we employed the content analysis method to examine interview data and construct a model. This research reveals four main barriers to digital transformation in small service business: lack of funding, lack of digital capability, lack of human resources, and technical barriers. We found there are four government roles for supporting digital transformation in small service business: build a digital platform for small service business, promote mobile/digital payment, provide digital training, and build a digital collaboration ecosystem. Additionally, based on this study’s findings, a model for barriers and government support of digital transformation in small service business is presented. This study contributes to the conceptual framework and management implications in the digital transformation field. The study’s findings provide insights to practitioners and suggest that the government could alter the current policies and programs to support digital transformation in small service business.

Tissue-resident memory T cells (T RM ) are a specialized subset of long-lived memory T cells that reside in peripheral tissues. However, the impact of T RM -related immunosurveillance on the tumor-immune microenvironment (TIME) and tumor progression across various non-small-cell lung cancer (NSCLC) patient populations is yet to be elucidated. Our comprehensive analysis of multiple independent single-cell and bulk RNA-seq datasets of patient NSCLC samples generated reliable, unique T RM signatures, through which we inferred the abundance of T RM in NSCLC. We discovered that T RM abundance is consistently positively correlated with CD4+ T helper 1 cells, M1 macrophages, and resting dendritic cells in the TIME. In addition, T RM signatures are strongly associated with immune checkpoint and stimulatory genes and the prognosis of NSCLC patients. A T RM -based machine learning model to predict patient survival was validated and an 18-gene risk score was further developed to effectively stratify patients into low-risk and high-risk categories, wherein patients with high-risk scores had significantly lower overall survival than patients with low-risk. The prognostic value of the risk score was independently validated by the Cancer Genome Atlas Program (TCGA) dataset and multiple independent NSCLC patient datasets. Notably, low-risk NSCLC patients with higher T RM infiltration exhibited enhanced T-cell immunity, nature killer cell activation, and other TIME immune responses related pathways, indicating a more active immune profile benefitting from immunotherapy. However, the T RM signature revealed low T RM abundance and a lack of prognostic association among lung squamous cell carcinoma patients in contrast to adenocarcinoma, indicating that the two NSCLC subtypes are driven by distinct TIMEs. Altogether, this study provides valuable insights into the complex interactions between T RM and TIME and their impact on NSCLC patient prognosis. The development of a simplified 18-gene risk score provides a practical prognostic marker for risk stratification.

Advanced beyond-silicon electronic technology requires both channel materials and also ultralow-resistance contacts to be discovered 1 , 2 . Atomically thin two-dimensional semiconductors have great potential for realizing high-performance electronic devices 1 , 3 . However, owing to metal-induced gap states (MIGS) 4 – 7 , energy barriers at the metal–semiconductor interface—which fundamentally lead to high contact resistance and poor current-delivery capability—have constrained the improvement of two-dimensional semiconductor transistors so far 2 , 8 , 9 . Here we report ohmic contact between semimetallic bismuth and semiconducting monolayer transition metal dichalcogenides (TMDs) where the MIGS are sufficiently suppressed and degenerate states in the TMD are spontaneously formed in contact with bismuth. Through this approach, we achieve zero Schottky barrier height, a contact resistance of 123 ohm micrometres and an on-state current density of 1,135 microamps per micrometre on monolayer MoS 2 ; these two values are, to the best of our knowledge, the lowest and highest yet recorded, respectively. We also demonstrate that excellent ohmic contacts can be formed on various monolayer semiconductors, including MoS 2 , WS 2 and WSe 2 . Our reported contact resistances are a substantial improvement for two-dimensional semiconductors, and approach the quantum limit. This technology unveils the potential of high-performance monolayer transistors that are on par with state-of-the-art three-dimensional semiconductors, enabling further device downscaling and extending Moore’s law. Electric contacts of semimetallic bismuth on monolayer semiconductors are shown to suppress metal-induced gap states and thus have very low contact resistance and a zero Schottky barrier height.

In the development of digital agriculture, agricultural robots play a unique role and confer numerous advantages in farming production. From the invention of the first industrial robots in the 1950s, robots have begun to capture the attention of both research and industry. Thanks to the recent advancements in computer science, sensing, and control approaches, agricultural robots have experienced a rapid evolution, relying on various cutting-edge technologies for different application scenarios. Indeed, significant refinements have been achieved by integrating perception, decision-making, control, and execution techniques. However, most agricultural robots continue to require intelligence solutions, limiting them to small-scale applications without quantity production because of their lack of integration with artificial intelligence. Therefore, to help researchers and engineers grasp the prevalent research status of agricultural robots, in this review we refer to more than 100 pieces of literature according to the category of agricultural robots under discussion. In this context, we bring together diverse agricultural robot research statuses and applications and discuss the benefits and challenges involved in further applications. Finally, directional indications are put forward with respect to the research trends relating to agricultural robots.

Cardiac macrophage contributes to the development of cardiac fibrosis, but factors that regulate cardiac macrophages transition and activation during this process remains elusive. Here we show, by single-cell transcriptomics, lineage tracing and parabiosis, that cardiac macrophages from circulating monocytes preferentially commit to macrophage-to-myofibroblast transition (MMT) under angiotensin II (Ang II)-induced hypertension, with accompanying increased expression of the RNA N6-methyladenosine demethylases, ALKBH5. Meanwhile, macrophage-specific knockout of ALKBH5 inhibits Ang II-induced MMT, and subsequently ameliorates cardiac fibrosis and dysfunction. Mechanistically, RNA immunoprecipitation sequencing identifies interlukin-11 (IL-11) mRNA as a target for ALKBH5-mediated m6A demethylation, leading to increased IL-11 mRNA stability and protein levels. By contrast, overexpression of IL11 in circulating macrophages reverses the phenotype in ALKBH5-deficient mice and macrophage. Lastly, targeted delivery of ALKBH5 or IL-11 receptor α (IL11RA1) siRNA to monocytes/macrophages attenuates MMT and cardiac fibrosis under hypertensive stress. Our results thus suggest that the ALKBH5/IL-11/IL11RA1/MMT axis alters cardiac macrophage and contributes to hypertensive cardiac fibrosis and dysfunction in mice, and thereby identify potential targets for cardiac fibrosis therapy in patients. Cardiac macrophage contributes to the onset of cardiac fibrosis, but the underneath mechanisms remain unclear. Here the authors show that mouse cardiac macrophages from circulating monocytes may trans-differentiate into myofibroblast under hypertensive conditions for fibrosis development, with an AKLBH5/IL11 molecular axis modulating this macrophage-to-myofibroblast transition.

Increasing evidence points to the tight relationship between alternative splicing (AS) and the salt stress response in plants. However, the mechanisms linking these two phenomena remain unclear. In this study, we have found that Salt-Responsive Alternatively Spliced gene 1 ( SRAS1 ), encoding a RING-Type E3 ligase, generates two splicing variants: SRAS1 . 1 and SRAS1 . 2 , which exhibit opposing responses to salt stress. The salt stress-responsive AS event resulted in greater accumulation of SRAS1 . 1 and a lower level of SRAS1 . 2 . Comprehensive phenotype analysis showed that overexpression of SRAS1 . 1 made the plants more tolerant to salt stress, whereas overexpression of SRAS1 . 2 made them more sensitive. In addition, we successfully identified the COP9 signalosome 5A (CSN5A) as the target of SRAS1. CSN5A is an essential player in the regulation of plant development and stress. The full-length SRAS1.1 promoted degradation of CSN5A by the 26S proteasome. By contrast, SRAS1.2 protected CSN5A by competing with SRAS1.1 on the same binding site. Thus, the salt stress-triggered AS controls the ratio of SRAS1.1/SRAS1.2 and switches on and off the degradation of CSN5A to balance the plant development and salt tolerance. Together, these results provide insights that salt-responsive AS acts as post-transcriptional regulation in mediating the function of E3 ligase.

Absolute lymphocyte count (ALC) is a crucial indicator of immunity in critical illness, but studies focusing on long-term outcomes in critically ill patients, particularly surgical patients, are still lacking. We sought to explore the association between week-one ALC and long-term mortality in critically ill surgical patients. We used the 2015-2020 critical care database of Taichung Veterans General Hospital (TCVGH), a referral hospital in central Taiwan, and the primary outcome was one-year all-cause mortality. We assessed the association between ALC and long-term mortality by measuring hazard ratios (HRs) with 95% confidence intervals (CIs). Furthermore, we used propensity score-matching and -weighting analyses, consisting of propensity score matching (PSM), inverse probability of treatment weighting (IPTW), and covariate balancing propensity score (CBPS), to validate the association. A total of 8052 patients were enrolled, with their one-year mortality being 24.2%. Cox regression showed that low ALC was independently associated with mortality (adjHR 1.140, 95% CI 1.091-1.192). Moreover, this association tended to be stronger among younger patients, patients with fewer comorbidities and lower severity. The association between low ALC and mortality in original, PSM, IPTW, and CBPS populations were 1.497 (95% CI 1.320-1.697), 1.391 (95% CI 1.169-1.654), 1.512 (95% CI 1.310-1.744), and 1.511 (95% CI 1.310-1.744), respectively. Additionally, the association appears to be consistent, using distinct cutoff levels to define the low ALC. We identified that early low ALC was associated with increased one-year mortality in critically ill surgical patients, and prospective studies are warranted to confirm the finding.