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Chronic wounds, which require prolonged healing periods, pose significant impacts on individuals with diabetes, vascular diseases, and high blood pressure. Simultaneous drainage and monitoring of wound exudate are vital for advanced wound management. However, recently reported smart dressings either lack integration of wound cleaning and monitoring functions or fail to achieve dynamic in situ monitoring of wound status, which hinders their ability to meet the demands of wound care. In this study, a smart bandage is introduced, which integrates a biocompatible liquid diode membrane with an ultrasensitive 3D polyaniline mesh (M-PANI)-based pH biosensor. The smart bandage allows for unidirectional drainage of wound exudate while dynamically sensing the wound pH environment. Specifically, the proposed smart bandage effectively cleans excessive wound exudate while providing real-time information on the wound status during the drainage process. The M-PANI-based pH biosensor demonstrates a high sensitivity of 61.5 mV/pH and a wide pH detection range from 4.0 to 10.0, encompassing the pH range of normal and infected wounds. Moreover, the sensing module exhibits excellent stability after 48 hours of dynamic testing and 28 days of storage, with only a 4.8% decline in the detected signal, and high repeatability with a device-to-device relative standard deviation (RSD) of 3.1%. To evaluate the practicality of this smart bandage, simulated skin and rats have been employed, and the results indicate the immense potential of this smart bandage for clinical applications. In conclusion, the present smart bandage demonstrates considerable promise for wound exudate cleaning and monitoring in advanced wound care and offers a promising method for home-based wound management.

The authors give a stochastic maximum principle for square-integrable optimal control of linear stochastic systems. The control domain is not necessarily convex and the cost functional can have a quadratic growth. In particular, they give a stochastic maximum principle for the linear quadratic optimal control problem.

Background Immune checkpoint inhibitors (ICIs) have demonstrated substantial therapeutic efficacy in the treatment of non-small cell lung cancer (NSCLC); however, their clinical application is associated with unique immune-related adverse effects (irAEs). Among these adverse events, immune checkpoint inhibitor-related pneumonitis (CIP) is rare yet serious, which may potentially result in severe respiratory failure, thereby requiring close clinical monitoring. Research specifically focusing on CIP in NSCLC patients treated with PD-1 inhibitors remain limited. This study targets this distinct cohort to comprehensively investigate the clinical and radiological determinants associated with overall survival, applying time-dependent covariate Cox regression to capture the dynamic impact of prognostic factors over time. Methods A total of 102 NSCLC participants who received immunotherapy with programmed cell death protein-1 (PD-1) inhibitors and then developed CIP were retrospectively enrolled in this study. Univariate and multivariate time-dependent covariate Cox regression models were constructed to determine associations between CIP features and survival benefits of CIP patients. Results The incidence of CIP was 15% (102/680) with a median onset time of 4.6 months. Fifty-one patients (50.0%) were identified as having organizing pneumonia (OP) pattern, followed by nonspecific interstitial pneumonia (NSIP) pattern in 28 patients (27.4%), hypersensitivity pneumonitis (HP) pattern in 6 patients (5.9%), and diffuse alveolar damage (DAD) pattern in 2 patients (2.0%). Additionally, 15 patients (14.7%) were classified as unclassifiable pattern. Kaplan-Meier analysis and Log-rank test indicated that CIP located around the tumor and with reticular opacity were associated with poorer prognosis ( P  = 0.023, P  = 0.013). Compared to those with CIP grades 2–4, patients with CIP grade 1 demonstrated survival benefit with border-line significance ( P  = 0.049). Multivariate time-dependent covariate Cox regression analysis showed that CIP improvement or not ( χ 2  = 6.81, P  = 0.009), percentage of neutrophils ( χ 2  = 24.13, P  < 0.001) and albumin ( χ 2  = 31.48, P  < 0.001) at the time of CIP diagnosis were independent influencing factors for overall survival (OS) in NSCLC patients with CIP. Conclusions CIP without improvement or resolution, a high percentage of neutrophils and elevated albumin level of peripheral blood examination were independent predictors for the prognosis of NSCLC patients, which may have an implication for treatment.

Plant-derived root exudates modulate plant-microbe interactions and may play an important role in pathogen suppression. Root exudates may, for instance, directly inhibit pathogens or alter microbiome composition. Here, we tested if plants modulate their root exudation in the presence of a pathogen and if these shifts alter the rhizosphere microbiome composition. We added exudates from healthy and Ralstonia solanacearum- infected tomato plants to an unplanted soil and followed changes in bacterial community composition. The presence of pathogen changed the exudation of phenolic compounds and increased the release of caffeic acid. The amendment of soils with exudates from the infected plants led to a development of distinct and less diverse soil microbiome communities. Crucially, we could reproduce similar shift in microbiome composition by adding pure caffeic acid into the soil. Caffeic acid further suppressed R. solanacearum growth in vitro . We conclude that pathogen-induced changes in root exudation profile may serve to control pathogen both by direct inhibition and by indirectly shifting the composition of rhizosphere microbiome.

Globally, 235 million people are impacted by humanitarian emergencies worldwide, presenting increased risk of experiencing a mental disorder. Our objective was to test the effectiveness of a brief group psychological treatment delivered by trained facilitators without prior professional mental health training in a disaster-prone setting. We conducted a cluster randomized controlled trial (cRCT) from November 25, 2018 through September 30, 2019. Participants in both arms were assessed at baseline, midline (7 weeks post-baseline, which was approximately 1 week after treatment in the experimental arm), and endline (20 weeks post-baseline, which was approximately 3 months posttreatment). The intervention was Group Problem Management Plus (PM+), a psychological treatment of 5 weekly sessions, which was compared with enhanced usual care (EUC) consisting of a family psychoeducation meeting with a referral option to primary care providers trained in mental healthcare. The setting was 72 wards (geographic unit of clustering) in eastern Nepal, with 1 PM+ group per ward in the treatment arm. Wards were eligible if they were in disaster-prone regions and residents spoke Nepali. Wards were assigned to study arms based on covariate constrained randomization. Eligible participants were adult women and men 18 years of age and older who met screening criteria for psychological distress and functional impairment. Outcomes were measured at the participant level, with assessors blinded to group assignment. The primary outcome was psychological distress assessed with the General Health Questionnaire (GHQ-12). Secondary outcomes included depression symptoms, posttraumatic stress disorder (PTSD) symptoms, \"heart-mind\" problems, social support, somatic symptoms, and functional impairment. The hypothesized mediator was skill use aligned with the treatment's mechanisms of action. A total of 324 participants were enrolled in the control arm (36 wards) and 319 in the Group PM+ arm (36 wards). The overall sample (N = 611) had a median age of 45 years (range 18-91 years), 82% of participants were female, 50% had recently experienced a natural disaster, and 31% had a chronic physical illness. Endline assessments were completed by 302 participants in the control arm (36 wards) and 303 participants in the Group PM+ arm (36 wards). At the midline assessment (immediately after Group PM+ in the experimental arm), mean GHQ-12 total score was 2.7 units lower in Group PM+ compared to control (95% CI: 1.7, 3.7, p < 0.001), with standardized mean difference (SMD) of -0.4 (95% CI: -0.5, -0.2). At 3 months posttreatment (primary endpoint), mean GHQ-12 total score was 1.4 units lower in Group PM+ compared to control (95% CI: 0.3, 2.5, p = 0.014), with SMD of -0.2 (95% CI: -0.4, 0.0). Among the secondary outcomes, Group PM+ was associated with endline with a larger proportion attaining more than 50% reduction in depression symptoms (29.9% of Group PM+ arm versus 17.3% of control arm, risk ratio = 1.7, 95% CI: 1.2, 2.4, p = 0.002). Fewer participants in the Group PM+ arm continued to have \"heart-mind\" problems at endline (58.8%) compared to the control arm (69.4%), risk ratio = 0.8 (95% CI, 0.7, 1.0, p = 0.042). Group PM+ was not associated with lower PTSD symptoms or functional impairment. Use of psychosocial skills at midline was estimated to explain 31% of the PM+ effect on endline GHQ-12 scores. Adverse events in the control arm included 1 suicide death and 1 reportable incidence of domestic violence; in the Group PM+ arm, there was 1 death due to physical illness. Study limitations include lack of power to evaluate gender-specific effects, lack of long-term outcomes (e.g., 12 months posttreatment), and lack of cost-effectiveness information. In this study, we found that a 5-session group psychological treatment delivered by nonspecialists modestly reduced psychological distress and depression symptoms in a setting prone to humanitarian emergencies. Benefits were partly explained by the degree of psychosocial skill use in daily life. To improve the treatment benefit, future implementation should focus on approaches to enhance skill use by PM+ participants. ClinicalTrials.gov NCT03747055.

Background Multidisciplinary treatment (MDT) consultations are essential for managing complex patients. However, resource and time constraints can limit their quality. Large language models (LLMs) have shown potential in assisting clinical decision-making, but their performance in complex MDT scenarios remains unclear. This study aims to evaluate the quality of MDT recommendations generated by ChatGPT compared to those provided by physicians. Methods Clinical data from 64 patient cases were retrospectively included in the study. ChatGPT was asked to provide specific MDT recommendations. 2 experienced physicians evaluated and scored the responses in a blinded manner across 5 aspects: comprehensiveness, accuracy, feasibility, safety, and efficiency, each assessed by 2 questions. Results The median overall score for ChatGPT was 41.0 out of 50.0, which was lower than the MDT physicians’ median score of 43.5 ( p  = 0.001). Compared to the MDT physicians’ responses, ChatGPT excelled in comprehensiveness ( p  < 0.001) but fell short in accuracy ( p  < 0.001), feasibility ( p  < 0.001), and efficiency ( p  = 0.003). Analysis of specific questions revealed that ChatGPT lacked the ability to reason through the etiologies of complex cases. Conclusion This study indicates that ChatGPT has potential in clinical MDT applications, particularly in demonstrating more comprehensive consideration of clinical factors. However, ChatGPT still has deficiencies in accuracy, which could lead to incorrect healthcare decisions. Therefore, further development and clinical validation of LLMs are necessary. Recognizing the current limitations of LLMs, it is essential to use them with caution in clinical practice. Trial registration Not applicable to the present retrospective study. For transparency, a related prospective extension is registered at ChiCTR (ChiCTR2400088563; registered on 21 August 2024).

Objective Individual variations in depressive disorder (DD) treatment responses highlight the need for early efficacy prediction to optimize regimens. The niacin skin flushing response (NSFR) is a potential DD biomarker, and elevated inflammatory cytokines are linked to DD. This study explored the association between blunted NSFR and DD symptom severity, and evaluated the predictive value of NSFR and inflammatory cytokines for early antidepressant efficacy. Methods Fifty DD patients were grouped as responders (≥ 50% Hamilton Depression Rating Scale reduction at week 2) or non-responders. Intergroup differences in NSFR index and inflammatory cytokines (Phospholipase A2 [PLA2], Cyclooxygenase-2 [COX-2]) were compared. Spearman’s correlation, binary logistic regression, and receiver operating characteristic (ROC) curves analyzed associations and predictive performance. Results (1) Baseline NSFR index was significantly negatively correlated with baseline HAMD score ( r = -0.736, p  < 0.001), indicating that the degree of NSFR attenuation reflects depression severity. (2) Both baseline NSFR index (AUC = 0.615) and COX-2 level (AUC = 0.725) independently predicted early treatment efficacy, with patients exhibiting lower baseline NSFR index or higher baseline COX-2 levels showing superior early efficacy. (3) The combined predictive model incorporating baseline NSFR index, COX-2, and PLA2 demonstrated optimal predictive performance (AUC = 0.786). Conclusion This study confirms that greater NSFR attenuation is associated with more severe depressive symptoms. Baseline NSFR and COX-2 hold promise as potential predictive biomarkers. Furthermore, the combined model based on NSFR and inflammatory cytokines exhibits superior predictive value, providing a potential basis for optimizing individualized DD treatment strategies and exploring anti-inflammatory therapeutic targets. Highlights Assessed NSFR and inflammatory cytokines for predicting early antidepressant efficacy. Confirmed that blunted NSFR reflects the severity of depressive disorder. Demonstrated combined NSFR + COX-2 + PLA2 model has optimal predictive performance. Offer basis for optimizing individualized DD treatment.

Respiratory infections caused by Pseudomonas aeruginosa are a major health problem globally. Current treatment for P . aeruginosa infections relies solely on antibiotics, but the rise of antibiotic-resistant strains necessitates an urgent need for a protective vaccine. Traditional parenteral vaccines, despite employing potent adjuvants aimed at serotype-dependent immunity, often fail to elicit the desired mucosal immune response. Thus, developing vaccines that target both localized mucosal and systemic immune responses represents a promising direction for future research on P . aeruginosa vaccination. In this study, we explored EPS301, the exopolysaccharide derived from the lung microbiota strain Lactobacillus plantarum WXD301, which exhibits excellent self-assembly properties, enabling the formation of homogeneous nanoparticles when encapsulating recombinant PcrV of P . aeruginosa , designated as EPS301@rPcrV. Notably, the EPS301 vector effectively enhanced antigen adhesion to the nasal and pulmonary mucosal tissues and prolonged antigen retention. Moreover, EPS301@rPcrV provided effective and sustained protection against P . aeruginosa pneumonia, surpassing the durability achieved with the \"gold standard\" cholera toxin adjuvant. The EPS301-adjuvanted vaccine formulation elicited robust mucosal IgA and Th17/γδ17 T cell responses, which exceeded those induced by the CTB-adjuvanted vaccination and were sustained for over 112 days. Additionally, Th 17 and γδ 17 resident memory T cells induced by EPS301@rPcrV were crucial for protection against P . aeruginosa challenge. Intriguingly, IL-17A knockout mice exhibited lower survival rates, impaired bacterial clearance ability, and exacerbated lung tissue damage upon EPS301 adjuvanted vaccination against P . aeruginosa -induced pneumonia, indicating an IL-17A-dependent protective mechanism. In conclusion, our findings provided direct evidence that EPS301@rPcrV mucosal vaccine is a promising candidate for future clinical application against P . aeruginosa -induced pulmonary infection.

Environmental risk assessment (ERA) is critical for protecting life by predicting population responses to contaminants. However, routine toxicity testing often examines only one genotype from surrogate species, potentially leading to inaccurate risk assessments, as natural populations typically consist of genetically diverse individuals. To evaluate the importance of intraspecific variation in translating toxicity testing to natural populations, we quantified the magnitude of phenotypic variation between 20 Daphnia magna clones exposed to two levels of microcystins, a cosmopolitan cyanobacterial toxin. We observed significant genetic variation in survival, growth, and reproduction, which increased under microcystins exposure. Simulations of survival showed that using a single genotype for toxicity tolerance estimates on average failed to produce accurate predictions within the 95% confidence interval over half of the time. Whole genome sequencing of the 20 clones tested for correlations between toxicological responses and genomic divergence, including candidate loci from prior gene expression studies. We found no overall correlations, indicating that clonal variation, rather than variation at candidate genes, predicts population-level responses to toxins. These results highlight the importance of incorporating broad intraspecific genetic variation, without focusing specifically on variation in candidate genes, into ERAs to more reliably predict how local populations will respond to contaminants.

To address the challenges of high experimental costs, complexity, and time consumption associated with pre-mixed combustible gas deflagration experiments under semi-open space obstacle conditions, a rapid temporal prediction method for flame propagation velocity based on Ranger-GRU neural networks is proposed. The deflagration experiment data are employed as the training dataset for the neural network, with the coefficient of determination (R2) and mean squared error (MSE) used as evaluation metrics to assess the predictive performance of the network. First, 108 sets of pre-mixed methane gas deflagration experiments were conducted, varying obstacle parameters to investigate methane deflagration mechanisms under different conditions. The experimental results demonstrate that obstacle-to-ignition source distance, obstacle shape, obstacle length, obstacle quantity, and thick and fine wire mesh obstacles all significantly influence flame propagation velocity. Subsequently, the GRU neural network was trained, and different activation functions (Sigmoid, Relu, PReLU) and optimizers (Lookahead, RAdam, Adam, Ranger) were incorporated into the backpropagation updating process of the network. The training results show that the Ranger-GRU neural network based on the PReLU activation function achieves the highest mean R2 value of 0.96 and the lowest mean MSE value of 7.16759. Therefore, the Ranger-GRU neural network with PReLU activation function can be a viable rapid prediction method for flame propagation velocity in pre-mixed methane gas deflagration experiments under semi-open space obstacle conditions.