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Composition of tissue types within a wound is a useful indicator of its healing progression. Tissue composition is clinically used in wound healing tools (eg, Bates-Jensen Wound Assessment Tool) to assess risk and recommend treatment. However, wound tissue identification and the estimation of their relative composition is highly subjective. Consequently, incorrect assessments could be reported, leading to downstream impacts including inappropriate dressing selection, failure to identify wounds at risk of not healing, or failure to make appropriate referrals to specialists. This study aimed to measure inter- and intrarater variability in manual tissue segmentation and quantification among a cohort of wound care clinicians and determine if an objective assessment of tissue types (ie, size and amount) can be achieved using deep neural networks. A data set of 58 anonymized wound images of various types of chronic wounds from Swift Medical's Wound Database was used to conduct the inter- and intrarater agreement study. The data set was split into 3 subsets with 50% overlap between subsets to measure intrarater agreement. In this study, 4 different tissue types (epithelial, granulation, slough, and eschar) within the wound bed were independently labeled by the 5 wound clinicians at 1-week intervals using a browser-based image annotation tool. In addition, 2 deep convolutional neural network architectures were developed for wound segmentation and tissue segmentation and were used in sequence in the workflow. These models were trained using 465,187 and 17,000 image-label pairs, respectively. This is the largest and most diverse reported data set used for training deep learning models for wound and wound tissue segmentation. The resulting models offer robust performance in diverse imaging conditions, are unbiased toward skin tones, and could execute in near real time on mobile devices. A poor to moderate interrater agreement in identifying tissue types in chronic wound images was reported. A very poor Krippendorff α value of .014 for interrater variability when identifying epithelization was observed, whereas granulation was most consistently identified by the clinicians. The intrarater intraclass correlation (3,1), however, indicates that raters were relatively consistent when labeling the same image multiple times over a period. Our deep learning models achieved a mean intersection over union of 0.8644 and 0.7192 for wound and tissue segmentation, respectively. A cohort of wound clinicians, by consensus, rated 91% (53/58) of the tissue segmentation results to be between fair and good in terms of tissue identification and segmentation quality. The interrater agreement study validates that clinicians exhibit considerable variability when identifying and visually estimating wound tissue proportion. The proposed deep learning technique provides objective tissue identification and measurements to assist clinicians in documenting the wound more accurately and could have a significant impact on wound care when deployed at scale.

Background The continued spread of insecticide resistance in mosquito vectors of malaria and arboviral diseases may lead to operational failure of insecticide-based interventions if resistance is not monitored and managed efficiently. This study aimed to develop and validate a new WHO glass bottle bioassay method as an alternative to the WHO standard insecticide tube test to monitor mosquito susceptibility to new public health insecticides with particular modes of action, physical properties or both. Methods A multi-centre study involving 21 laboratories worldwide generated data on the susceptibility of seven mosquito species ( Aedes aegypti , Aedes albopictus , Anopheles gambiae sensu stricto [ An. gambiae s.s.], Anopheles funestus, Anopheles stephensi, Anopheles minimus and Anopheles albimanus ) to seven public health insecticides in five classes, including pyrethroids (metofluthrin, prallethrin and transfluthrin), neonicotinoids (clothianidin), pyrroles (chlorfenapyr), juvenile hormone mimics (pyriproxyfen) and butenolides (flupyradifurone), in glass bottle assays. The data were analysed using a Bayesian binomial model to determine the concentration–response curves for each insecticide–species combination and to assess the within-bioassay variability in the susceptibility endpoints, namely the concentration that kills 50% and 99% of the test population (LC 50 and LC 99 , respectively) and the concentration that inhibits oviposition of the test population by 50% and 99% (OI 50 and OI 99 ), to measure mortality and the sterilizing effect, respectively. Results Overall, about 200,000 mosquitoes were tested with the new bottle bioassay, and LC 50 /LC 99 or OI 50 /OI 99 values were determined for all insecticides. Variation was seen between laboratories in estimates for some mosquito species–insecticide combinations, while other test results were consistent. The variation was generally greater with transfluthrin and flupyradifurone than with the other compounds tested, especially against Anopheles species. Overall, the mean within-bioassay variability in mortality and oviposition inhibition were < 10% for most mosquito species-insecticide combinations. Conclusion Our findings, based on the largest susceptibility dataset ever produced on mosquitoes, showed that the new WHO bottle bioassay is adequate for evaluating mosquito susceptibility to new and promising public health insecticides currently deployed for vector control. The datasets presented in this study have been used recently by the WHO to establish 17 new insecticide discriminating concentrations (DCs) for either Aedes spp. or Anopheles spp. The bottle bioassay and DCs can now be widely used to monitor baseline insecticide susceptibility of wild populations of vectors of malaria and Aedes- borne diseases worldwide. Graphical abstract

This study explores the mediating role of resilience in the relationship between spirituality and happiness among environmental engineering students in northern Peru, emphasizing its implications for sustainability in education and professional development. Through a quantitative cross-sectional study involving 392 students from public and private universities, two key hypotheses were tested: the direct influence of spirituality on happiness and the mediating effect of resilience. Data were gathered using validated instruments, including the Personal Spirituality Scale (SPI), Connor-Davidson Brief Resilience Scale (CD-RISC 10), and Subjective Happiness Scale (SHS). Structural equation modeling revealed that spirituality has a significant direct effect on happiness and an indirect effect through resilience. The model showed high explanatory power, with spirituality explaining 87% of the variance in resilience, and both variables accounting for 76% of the variance in happiness. These findings highlight the importance of promoting spiritual and resilience-building practices as key strategies for enhancing subjective well-being, a critical factor for preparing sustainable professionals capable of addressing complex environmental challenges. This study contributes to the understanding of how spiritual resources and resilience mechanisms can support the development of socially and psychologically sustainable future engineers.

Purpose To develop and validate a continuous non-invasive blood pressure (BP) monitoring system using photoplethysmography (PPG) technology through pulse oximetry (PO). Methods This prospective study was conducted at a critical care department and post-anesthesia care unit of a university teaching hospital. Inclusion criteria were critically ill adult patients undergoing invasive BP measurement with an arterial catheter and PO monitoring. Exclusion criteria were arrhythmia, imminent death condition, and disturbances in the arterial or the PPG curve morphology. Arterial BP and finger PO waves were recorded simultaneously for 30 min. Systolic arterial pressure (SAP), mean arterial pressure (MAP), and diastolic arterial pressure (DAP) were extracted from computer-assisted arterial pulse wave analysis. Inherent traits of both waves were used to construct a regression model with a Deep Belief Network-Restricted Boltzmann Machine (DBN-RBM) from a training cohort of patients and in order to infer BP values from the PO wave. Bland–Altman analysis was performed. Results A total of 707 patients were enrolled, of whom 135 were excluded. Of the 572 studied, 525 were assigned to the training cohort (TC) and 47 to the validation cohort (VC). After data processing, 53,708 frames were obtained from the TC and 7,715 frames from the VC. The mean prediction biases were −2.98 ± 19.35, −3.38 ± 10.35, and −3.65 ± 8.69 mmHg for SAP, MAP, and DAP respectively. Conclusions BP can be inferred from PPG using DBN-RBM modeling techniques. The results obtained with this technology are promising, but its intrinsic variability and its wide limits of agreement do not allow clinical application at this time.

This study examines the determinants of the sustainable use of artificial intelligence (AI) among university professors in Peru. This research adopted a quantitative approach through a cross-sectional empirical–explanatory study, employing a structural equation model. Data were collected from 368 professors from eight Peruvian universities using a structured questionnaire that assessed six main constructs: attitude toward AI, prejudice against AI, facilitating conditions, use of AI, teaching concerns, and ethical perception. While the results reveal significant correlational relationships—with attitude toward AI, facilitating conditions, and prejudice against AI showing a significant association with its sustainable use, and the use of AI showing a significant relationship with professors’ ethical perceptions—the cross-sectional nature of this study precludes causal inferences. No significant relationship was found between the use of AI and teaching concerns. Additionally, demographic variables such as gender and age did not exhibit significant moderating effects. These findings contribute to understanding the factors related to the sustainable adoption of AI in higher education and provide valuable insights for the development of effective institutional strategies in the Latin American context.

Background: Anticipating and avoiding preventable intrahospital cardiac arrest and clinical deterioration are important priorities for international healthcare systems and institutions. One of the internationally followed strategies to improve this matter is the introduction of the Rapid Response Systems (RRS). Although there is vast evidence from the international community, the evidence reported in a Spanish context is scarce. Methods: A nationwide cross-sectional research consisting of a voluntary 31-question online survey was performed. The Spanish Society of Intensive, Critical and Coronary Care Medicine (SEMICYUC) supported the research. Results: We received 62 fully completed surveys distributed within 13 of the 17 regions and two autonomous cities of Spain. Thirty-two of the participants had an established Rapid Response Team (RRT). Common frequency on measuring vital signs was at least once per shift but other frequencies were contemplated (48.4%), usually based on professional criteria (69.4%), as only 12 (19.4%) centers used Early Warning Scores (EWS) or automated alarms on abnormal parameters. In the sample, doctors, nurses (55%), and other healthcare professionals (39%) could activate the RRT via telephone, but only 11.3% of the sample enacted this at early signs of deterioration. The responders on the RRT are the Intensive Care Unit (ICU), doctors, and nurses, who are available 24/7 most of the time. Concerning the education and training of general ward staff and RRT members, this varies from basic to advanced and specific-specialized level, simulating a growing educational methodology among participants. A great number of participants have emergency resuscitation equipment (drugs, airway adjuncts, and defibrillators) in their general wards. In terms of quality improvement, only half of the sample registered RRT activity indicators. In terms of the use of communication and teamwork techniques, the most used is clinical debriefing in 29 centers. Conclusions: In terms of the concept of RRS, we found in our context that we are in the early stages of the establishment process, as it is not yet a generalized concept in most of our hospitals. The centers that have it are in still in the process of maturing the system and adapting themselves to our context.

Background Current patient monitoring technologies are crucial for delivering personalised and timely care and are critical in achieving the best health outcomes while maintaining high care standards. However, these technologies also present several challenges affecting patients and healthcare professionals. Information overload Healthcare providers often deal with excess data, making it challenging to identify the most critical patient information quickly. This may lead to delays in necessary interventions and potentially poorer patient outcomes. Alarm fatigue Many patient monitoring systems trigger frequent false alarms. This high incidence can cause healthcare providers to become desensitised, potentially leading to slower response times or overlooked important alerts. Integration challenges Current systems often need more seamless integration with other healthcare technologies, making it difficult for healthcare providers to have a cohesive view of the patient’s health. This lack of integration can impair care coordination and increase workloads. This paper presents the findings from a group of experts who described the state of the art of patient monitoring and discussed potential solutions and new pathways for developing these technologies.

Azospirillum are prominent plant growth-promoting rhizobacteria (PGPR) extensively used as phytostimulatory crop inoculants, but only few studies are dealing with Azospirillum -containing mixed inocula involving more than two microorganisms. We compared here three prominent Azospirillum strains as part of three-component consortia including also the PGPR Pseudomonas fluorescens F113 and a mycorrhizal inoculant mix composed of three Glomus strains. Inoculant colonization of maize was assessed by quantitative PCR, transcription of auxin synthesis gene ipdC (involved in phytostimulation) in Azospirillum by RT-PCR, and effects on maize by secondary metabolic profiling and shoot biomass measurements. Results showed that phytostimulation by all the three-component consortia was comparable, despite contrasted survival of the Azospirillum strains and different secondary metabolic responses of maize to inoculation. Unexpectedly, the presence of Azospirillum in the inoculum resulted in lower phytostimulation in comparison with the Pseudomonas – Glomus two-component consortium, but this effect was transient. Azospirillum 's ipdC gene was transcribed in all treatments, especially with three-component consortia, but not with all plants and samplings. Inoculation had no negative impact on the prevalence of mycorrhizal taxa in roots. In conclusion, this study brought new insights in the functioning of microbial consortia and showed that Azospirillum – Pseudomonas – Glomus three-component inoculants may be useful in environmental biotechnology for maize growth promotion.

The genus Burkholderia comprises more than 60 species isolated from a wide range of niches. Although they have been shown to be diverse and ubiquitously distributed, most studies have thus far focused on the pathogenic species due to their clinical importance. However, the increasing number of recently described Burkholderia species associated with plants or with the environment has highlighted the division of the genus into two main clusters, as suggested by phylogenetical analyses. The first cluster includes human, animal, and plant pathogens, such as Burkholderia glumae, Burkholderia pseudomallei, and Burkholderia mallei, as well as the 17 defined species of the Burkholderia cepacia complex, while the other, more recently established cluster comprises more than 30 nonpathogenic species, which in most cases have been found to be associated with plants, and thus might be considered to be potentially beneficial. Several species from the latter group share characteristics that are of use when associating with plants, such as a quorum sensing system, the presence of nitrogen fixation and/or nodulation genes, and the ability to degrade aromatic compounds. This review examines the commonalities in this growing subgroup of Burkholderia species and discusses their prospective biotechnological applications.