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Making concrete sustainable globally requires technology transfer

Injury alters sensory, motor, and integrative elements underlying operant conditioning in the medicinal leech

Studies of pain/nociception often rely on simple reflexes to assess pain-related changes in behavior. However, there is considerable interest in utilizing more complex, self-initiated behaviors in place of stimulus-evoked reflexes. In this study we report an operant conditioning assay using Hirudo verbana (the medicinal leech) to assess the effects of injury on motivational and cognitive processes. Animals were placed in an arena consisting of an illuminated and a dark chamber with a connecting section in between. The connecting section was partially filled with gravel, which acted as an obstacle and delayed escape from the illuminated to the dark side. With repeated experience H. verbana learned to overcome the gravel obstacle, reflected as a decreased escape latency from the illuminated chamber. The capacity for this enhanced escape behavior was retained for up to two hours. In animals that received an injury to the posterior sucker, learning and memory of this operant escape task was disrupted. Injured animals also exhibited mechanosensory sensitization, changes in locomotion, changes in exploratory behavior, and increased negative phototaxis. Over 12 days, changes in locomotion, exploratory behavior, and phototaxis recovered to pre-injury levels, although mechanosensory sensitization remained. Disruptions in cognitive behavior also recovered during this period with the capacity for operant conditioning returning six days after injury and two hour retention of conditioning returning by day 12. This study shows that injury produces a complex and coordinated set of sensory, motor, and integrative changes in H. verbana that may be relevant to understanding the biological processes behind pain in vertebrates.

Do our observations make reality happen?

Towards accurate differential diagnosis with large language models

Abstract A comprehensive differential diagnosis is a cornerstone of medical care that is often reached through an iterative process of interpretation that combines clinical history, physical examination, investigations and procedures. Interactive interfaces powered by large language models present new opportunities to assist and automate aspects of this process1. Here we introduce the Articulate Medical Intelligence Explorer (AMIE), a large language model that is optimized for diagnostic reasoning, and evaluate its ability to generate a differential diagnosis alone or as an aid to clinicians. Twenty clinicians evaluated 302 challenging, real-world medical cases sourced from published case reports. Each case report was read by two clinicians, who were randomized to one of two assistive conditions: assistance from search engines and standard medical resources; or assistance from AMIE in addition to these tools. All clinicians provided a baseline, unassisted differential diagnosis prior to using the respective assistive tools. AMIE exhibited standalone performance that exceeded that of unassisted clinicians (top-10 accuracy 59.1% versus 33.6%, P = 0.04). Comparing the two assisted study arms, the differential diagnosis quality score was higher for clinicians assisted by AMIE (top-10 accuracy 51.7%) compared with clinicians without its assistance (36.1%; McNemar’s test: 45.7, P < 0.01) and clinicians with search (44.4%; McNemar’s test: 4.75, P = 0.03). Further, clinicians assisted by AMIE arrived at more comprehensive differential lists than those without assistance from AMIE. Our study suggests that AMIE has potential to improve clinicians’ diagnostic reasoning and accuracy in challenging cases, meriting further real-world evaluation for its ability to empower physicians and widen patients’ access to specialist-level expertise.

Linalool-based silver nanoconjugates as potential therapeutics for glioblastoma: in silico and in vitro insights

Glioblastoma is the most predominant type of brain tumor, and resistance to medication has hampered the effectiveness of chemotherapy for gliomas. Acyclic monoterpene alcohol, linalool, has a range of pharmacological properties. The present study aimed to evaluate the impact of linalool and its nanoformulation on glioblastoma cell proliferation. DFT and ADMET analyses were used to initially assess the physiochemical characteristics of linalool and the produced silver nanoconjugates, LN@AgNPs. STRING database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to narrow the 6 genes involved in glioblastoma and underwent for molecular docking study. Using AutoDock Vina 1.5.7, ligands were docked to the interaction site of selected targets. Top scored complexes PD-L1/Ligands and PTEN/ligands were simulated using molecular dynamics. The results revealed that LN@AgNPs produced a more stable complex, because metallic bonds are more robust and durable than hydrogen bonds, which give metals their distinctive strength and stability. To confirm the cytotoxicity of the compound against GBM cell line SF-767, linalool and LN@AgNPs were evaluated by in vitro study to check the expression at the IC50 concentration of top scored selected genes. The results indicated that the cytotoxic effects of linalool and LN@AgNPs were concentration dependent. In the SF-767 cancer cell line, linalool and LN@AgNPs with IC50 (33.14 µg/mL and 22.12 µg/mL respectively) values downregulated PD-L1 expression and increased PTEN expression. In conclusion phytocompounds conjugated with AgNPs increased cytotoxicity and inhibition index in glioblastoma cells. Therefore, LN@AgNPs may be a viable option for cancer treatment.

Cross-modal interactive and global awareness fusion network for RGB-D salient object detection

The RGB-D salient object detection technique has garnered significant attention in recent years due to its excellent performance. It outperforms salient object detection methods that rely solely on RGB images by leveraging the geometric morphology and spatial layout information from depth images. However, the existing RGB-D detection model still encounters difficulties in accurately recognising and highlighting salient objects when facing complex scenes containing multiple or small objects. In this study, a Cross-modal Interactive and Global Awareness Fusion Network for RGB-D Salient Object Detection, named CIGNet, is proposed. Specifically, convolutional neural networks (CNNs), which are good at extracting local details, and an attention mechanism, which efficiently integrates global information, are utilized to design two fusion methods for RGB and depth images. One of these methods, the Cross-modal Interaction Fusion Module (CIFM), employs depth separable convolution and common-dimensional dynamic convolution to extract rich edge contours and texture details from low-level features. The Global Awareness Fusion Module (GAFM) is designed to relate high-level features between RGB and depth features so as to improve the model’s understanding of complex scenes. In addition, prediction mapping is generated through a step-by-step decoding process carried out by the Multi-layer Convolutional Fusion Module (MCFM), which gradually yields finer detection results. Finally, comparing 12 mainstream methods on six public benchmark datasets demonstrates superior robustness and accuracy.

Retraction: Anti H. pylori, anti-secretory and gastroprotective effects of Thymus vulgaris on ethanol-induced gastric ulcer in Sprague Dawley rats

Protocol for EpiCom: A phase 3b/4 study of behavioral outcomes following adjunctive cannabidiol for the management of tuberous sclerosis complex-associated neuropsychiatric disorders (TAND)

Tuberous sclerosis complex (TSC)-associated neuropsychiatric disorders (TAND) affect ≈90% of individuals with TSC and significantly reduce their quality of life (QoL). However, there are limited studies assessing pharmacotherapy for TAND. A plant-derived highly purified pharmaceutical formulation of cannabidiol (CBD; Epidiolex®/Epidyolex® oral solution) is approved for seizures associated with TSC. Anecdotal evidence also suggests psychiatric, neuropsychological, and behavioral benefits of CBD. EpiCom (Epilepsy Comorbidities; NCT05864846; EU-CT, 2023-507426-17), a multicenter, open-label, phase 3b/4 study, with hybrid decentralized approach, was designed in collaboration with patient advisory groups and healthcare professionals to evaluate behavioral and other outcomes following adjunctive CBD treatment in individuals with TSC-associated seizures. EpiCom will enroll participants, aged 1–65 years (United States [US]) or 2–65 years (United Kingdom [UK], Canada, and Poland), who are starting CBD for seizures and have moderate/severe behavioral challenges according to the Caregiver Global Impression of Severity scale at screening. Participants will receive CBD (up to 25 mg/kg/d based on individual response and tolerability) in addition to their standard of care (SoC) for 26 weeks, after which participants may choose to continue CBD with SoC or SoC alone for an additional 26 weeks. Key efficacy endpoints include change from baseline on the Aberrant Behavior Checklist (e.g., irritability subscale) and the most problematic behavior on the TAND-Self-Report, Quantified Checklist. Changes in executive function, sleep, QoL, family functioning, seizure outcomes (severity, responder rates, seizure-free days), retention rate, and safety will be evaluated. The trial will enroll ≈75 participants at ≈20 sites across the US, the UK, Canada, and Poland. EpiCom will assess the changes in behavioral and other outcomes associated with TAND and seizure outcomes, including overall symptom severity and treatment retention, following adjunctive CBD treatment in individuals with TSC-associated seizures. The results will inform future studies evaluating pharmacotherapy for behavioral outcomes in similar populations.

Want to enhance lab safety? Try a little role playing first

Making slabs and sleepers from old appliances

How DEI is misunderstood — and the real route to gender equality

Swinging lever mechanism of myosin directly shown by time-resolved cryo-EM

Abstract Myosins produce force and movement in cells through interactions with F-actin1. Generation of movement is thought to arise through actin-catalysed conversion of myosin from an ATP-generated primed (pre-powerstroke) state to a post-powerstroke state, accompanied by myosin lever swing2,3. However, the initial, primed actomyosin state has never been observed, and the mechanism by which actin catalyses myosin ATPase activity is unclear. Here, to address these issues, we performed time-resolved cryogenic electron microscopy (cryo-EM)4 of a myosin-5 mutant having slow hydrolysis product release5,6. Primed actomyosin was predominantly captured 10 ms after mixing primed myosin with F-actin, whereas post-powerstroke actomyosin predominated at 120 ms, with no abundant intermediate states detected. For detailed interpretation, cryo-EM maps were fitted with pseudo-atomic models. Small but critical changes accompany the primed motor binding to actin through its lower 50-kDa subdomain, with the actin-binding cleft open and phosphate release prohibited. Amino-terminal actin interactions with myosin promote rotation of the upper 50-kDa subdomain, closing the actin-binding cleft, and enabling phosphate release. The formation of interactions between the upper 50-kDa subdomain and actin creates the strong-binding interface needed for effective force production. The myosin-5 lever swings through 93°, predominantly along the actin axis, with little twisting. The magnitude of lever swing matches the typical step length of myosin-5 along actin7. These time-resolved structures demonstrate the swinging lever mechanism, elucidate structural transitions of the power stroke, and resolve decades of conjecture on how myosins generate movement.

What puzzles people, past and present: Books in brief

Exposure to vibrotactile music improves audiometric performances in individuals with cochlear implants

Publisher Correction: A chain mediation model reveals the association between parental mediation and smartphone addiction of Chinese adolescents

Evaluating the impact of agricultural abandonment on flood mitigation functions

Abstract The flood mitigation functions of agricultural ecosystems are crucial in Ecosystem-based Disaster Risk Reduction (Eco-DRR). However, agricultural ecosystems, particularly in developed countries, face increasing abandonment in recent years. This study examined how agricultural abandonment affects Eco-DRR functions in central Japan. In paddy fields, water retention is key for flood mitigation, while in dry farmlands, water infiltration is vital. We analyzed the relationship between abandonment ratios and flood frequency across 132 municipalities in Japan. Results indicated that abandonment had little or no impact on Eco-DRR functions both paddy fields and dry farmlands. For paddy fields, this may be due to high levels of modernization or a low abandonment rate, which can enhance Eco-DRR functions. In dry farmlands, abandonment likely does not affect flood mitigation because it does not impair infiltration functions. Thus, conserving agricultural land is beneficial for Eco-DRR, even if abandoned. Land managers should avoid converting abandoned areas into residential zones or installing artificial structures.

Towards conversational diagnostic artificial intelligence

Abstract At the heart of medicine lies physician–patient dialogue, where skillful history-taking enables effective diagnosis, management and enduring trust1,2. Artificial intelligence (AI) systems capable of diagnostic dialogue could increase accessibility and quality of care. However, approximating clinicians’ expertise is an outstanding challenge. Here we introduce AMIE (Articulate Medical Intelligence Explorer), a large language model (LLM)-based AI system optimized for diagnostic dialogue. AMIE uses a self-play-based3 simulated environment with automated feedback for scaling learning across disease conditions, specialties and contexts. We designed a framework for evaluating clinically meaningful axes of performance, including history-taking, diagnostic accuracy, management, communication skills and empathy. We compared AMIE’s performance to that of primary care physicians in a randomized, double-blind crossover study of text-based consultations with validated patient-actors similar to objective structured clinical examination4,5. The study included 159 case scenarios from providers in Canada, the United Kingdom and India, 20 primary care physicians compared to AMIE, and evaluations by specialist physicians and patient-actors. AMIE demonstrated greater diagnostic accuracy and superior performance on 30 out of 32 axes according to the specialist physicians and 25 out of 26 axes according to the patient-actors. Our research has several limitations and should be interpreted with caution. Clinicians used synchronous text chat, which permits large-scale LLM–patient interactions, but this is unfamiliar in clinical practice. While further research is required before AMIE could be translated to real-world settings, the results represent a milestone towards conversational diagnostic AI.

Intradialytic optical assessment of C-mannosyl tryptophan removal using spent dialysate

Abstract C-mannosyl tryptophan (CMW), also known as C-glycosyltryptophan, is a novel biomarker that is strongly correlated to chronic kidney disease (CKD) incidence and progression risk and mortality among earlier stages of CKD patients prior to end stage kidney disease. This study determined concentrations of CMW in blood and spent dialysate of CKD patients on chronic hemodialysis (HD) for the first time, and investigated the possibility for optical estimation of CMW concentrations in spent dialysate, its intradialytic removal and time-averaged concentration (TAC) of CMW based on optical measurements of spent dialysate. In total, 264 pre- and postdialysis blood samples, and 528 spent dialysate samples from 88 HD sessions of 22 patients were analyzed using high pressure liquid chromatography and spectrophotometry. We identified that CMW concentrations in CKD patients on chronic HD are over 10 times higher compared to earlier reported CMW concentrations in healthy subjects. The concentration of CMW in spent dialysate can be monitored based on spectrophotometric analysis of spent dialysate (r > 0.939, standard error: 0.07 μmol/L) and it is possible to evaluate CMW-based HD adequacy parameters, such as reduction ratio, mass of total removed solute, and TAC without blood sampling. In future, optical monitoring of CMW could be potentially used to improve clinical management of hemodialysis patients.

Publisher Correction: Improving landslide susceptibility prediction through ensemble recursive feature elimination and meta-learning framework

Role of FinTech and technological innovation towards energy, growth, and environment nexus in G20 economies

Abstract The current global consumption scenario is characterized as an energy-intensive economic development, indicating a rising mismatch in the harmonious relationship between individuals and the environment. The mismatch is caused by unsustainable consumption practices that do not take into account long-term ecological repercussions. To address this mismatch, it is necessary to turn toward sustainable energy use, greener technologies, and more responsible resource management, with the goal of balancing human economic progress with environmental care. Therefore, this study examines the influence of FinTech and technological innovations on the energy-growth-environment nexus in the context of G-20 economies for the time span of 2005- 2022. The study employs the panel vector autoregressive (PVAR) model in the generalized method of moment (GMM) approach to explore the interrelationship among the variables. From the findings, it was concluded that FinTech has a positive impact on the energy-growth-environment nexus. Similar to FinTech, technological innovation also has favourable influence on the energy-growth-environment nexus. Finally, there exist positive influence of energy on growth and environment, whereas rising carbon emissions exerts negative influence on growth and renewable energy consumption. From the policy standpoint, authorities can catalyse a more sustainable and inclusive future by encouraging collaboration among the fintech, technological advancement, energy, and environmental sectors.