Firefly algorithm-based LSTM model for Guzheng tunes switching with big data analysis.

Guzheng tune progression involves intricately harmonizing melodic motif transitions. Effectively navigating this vast creative possibility space to expose musically consistent elaborations presents challenges. We develop a specialized large long short-term memory (LSTM) model for generating musically consistent Guzheng tune transitions. First, we propose novel firefly algorithm (FA) enhancements, e.g., adaptive diversity preservation and adaptive swim parameters, to boost exploration effectiveness for navigating the vast creative combinatorics when generating Guzheng tune transitions. Then, we develop a specialized stacked LSTM architecture incorporating residual connections and conditioned embedding vectors that can leverage long-range temporal dependencies in Guzheng music patterns, including unsupervised learning of concise Guzheng-specific melody embedding vectors via a variational autoencoder, encapsulating unique harmonic signatures from performance descriptors to provide style guidance. Finally, we use LSTM networks to develop adversarial generative large models that enable realistic synthesis and evaluation of Guzheng tunes switching. We gather an extensive 10+ hour corpus of solo Guzheng recordings spanning 230 musical pieces, 130 distinguished performing artists, and 600+ audio tracks. Simultaneously, we conduct thorough Guzheng data analysis. Comparative assessments against strong baselines over systematic musical metrics and professional listeners validate significant generation fidelity improvements. Our model achieves a 63 % reduction in reconstruction error compared to the standard FA optimization after 1000 iterations. It also outperforms baselines in capturing characteristic motifs, maintaining modality coherence with under 2 % dissonant pitch errors, and retaining desired rhythmic cadences. User studies further confirm the superior naturalness, novelty, and stylistic faithfulness of the generated tune transitions, with ratings close to real data.

[Implementation of Lung Nodule Detection Model Based on Incremental Meta-Learning].

In response to the issue that traditional lung nodule detection models cannot dynamically optimize and update with the increase of new data, a new lung nodule detection model-task incremental meta-learning model (TIMLM) is proposed. This model comprises of two loops: the inner loop imposes incremental learning regularization update constraints, while the outer loop employs a meta-update strategy to sample old and new knowledge and learn a set of generalized parameters that adapt to old and new data. Under the condition that the main structure of the model is not changed as much as possible, it preserves the old knowledge that was learned previously. Experimental verification on the publicly available lung dataset showed that, compared with traditional deep network models and mainstream incremental models, TIMLM has greatly improved in terms of accuracy, sensitivity, and other indicators, demonstrating good continuous learning and anti-forgetting capabilities.

Which biofilm reactor is suitable for degradation of 2,4-dimethylphenol, focusing on bacteria, algae, or a combination of bacteria-algae?

Effectively treating phenolic substances is a crucial task in environmental protection. This study aims to determine whether bacterial-algae biofilm reactors offer superior treatment efficacy compared to traditional activated sludge and biofilm reactors. The average degradation ratios of 2,4-dimethylphenol (40, 70, 150, 300, and 230 mg/L) were found to be 98 %, 99 %, 92.1 %, 84.7 %, and 63.7 % respectively. The bacterial-algae biofilm demonstrates a higher tolerance to toxicity, assimilation ability, and efficacy recovery ability. The cell membrane of Chlorella in the bacteria-algae biofilm is not easily compromised, thus ensuring a stable pH environment. High concentrations of tightly bound extracellular polymers (TB-EPS) enhance the efficacy in treating toxic pollutants, promote the stable structure. Intact Chlorella, bacilli, and EPS were observed in bacterial-algal biofilm. The structural integrity of bacteria-algae consistently enhances its resistance to the inhibitory effects of high concentrations of phenolic compounds. Cloacibacterium, Comamonas, and Dyella were the main functional bacterial genera that facilitate the formation of bacterial-algal biofilms and the degradation of phenolic compounds. The dominant microalgal families include Aspergillaceae, Chlorellales, Chlorellaceae, and Scenedesmaceae have certain treatment effects on phenolic substances. Chlorellales and Chlorellaceae have the ability to convert NH4+-N. The Aspergillaceae is also capable of generating synergistic effects with Chlorellales, Chlorellaceae, and Scenedesmaceae, thereby establishing a stable bacterial-algal biofilm system.

Scinderin is a potential prognostic biomarker and correlated with immunological regulation: from pan-cancer analysis to liver hepatocellular carcinoma.

Aim: This study aimed to systematically dissect the role of Scinderin (SCIN) in tumorigenesis. Methods: Bioinformatics techniques were employed based on cancer data from TCGA, ENCORI, HPA, GEPIA2, UALCAN, Kaplan-Meier plotter, TIMER, TISIDB, cBioPortal, HCCDB, GeneMANIA and LinkedOmics database. Experiments in vitro and in vivo were conducted to dissect the role of SCIN in liver hepatocellular carcinoma (LIHC). Results: Significantly differential expression of SCIN was found in nine types of cancers, including LIHC. Through pan-cancer analysis, the correlations between SCIN expression with prognosis and immune cell infiltration were proven, especially in LIHC, ovarian serous cystadenocarcinoma and lung adenocarcinoma. The highest frequency of alteration in SCIN (6.81%) was seen in patients with uterine corpus endometrial carcinoma, in which "mutation" was the predominant type, with a frequency of about 5.29%; meanwhile, S673F and S381Y were the two most frequent mutation sites. Furthermore, the abnormal expression of SCIN exhibited a strong relationship with immune cell subtypes, immune checkpoint genes, tumor mutation burden, microsatellite instability, neoantigen, molecular subtypes, mismatch repair signatures and DNA methyl-transferase in different cancer types. Through comparative analysis, we discovered that SCIN was dramatically up-regulated in LIHC, and associated with poor survival. Experiments in vitro and in vivo suggested the knockdown of SCIN could suppress tumor cell proliferation and improve the survival rate partly in animal models. Conclusion: This study reveals SCIN may be a promising biomarker for prognosis and treatment in certain cancers, especially in LIHC.

Quality assessment of abdominal CT images: an improved ResNet algorithm with dual-attention mechanism.

OBJECTIVES: To enhance medical image classification using a Dual-attention ResNet model and investigate the impact of attention mechanisms on model performance in a clinical setting. METHODS: We utilized a dataset of medical images and implemented a Dual-attention ResNet model, integrating self-attention and spatial attention mechanisms. The model was trained and evaluated using binary and five-level quality classification tasks, leveraging standard evaluation metrics. RESULTS: Our findings demonstrated substantial performance improvements with the Dual-attention ResNet model in both classification tasks. In the binary classification task, the model achieved an accuracy of 0.940, outperforming the conventional ResNet model. Similarly, in the five-level quality classification task, the Dual-attention ResNet model attained an accuracy of 0.757, highlighting its efficacy in capturing nuanced distinctions in image quality. CONCLUSIONS: The integration of attention mechanisms within the ResNet model resulted in significant performance enhancements, showcasing its potential for improving medical image classification tasks. These results underscore the promising role of attention mechanisms in facilitating more accurate and discriminative analysis of medical images, thus holding substantial promise for clinical applications in radiology and diagnostics.

Surgical Outcomes in Patients with Endolymphatic Sac Tumors: A Single-Center Experience of 29 Patients.

OBJECTIVE: To analyze the clinical features and surgical outcomes of patients with endolymphatic sac tumors (ELSTs). STUDY DESIGN: Single institution retrospective cohort study. METHODS: The clinical data of 29 patients with 30 ELSTs who underwent surgery were retrospectively reviewed. Information on patient demographics, tumor size and grade, intraoperative blood loss volume, hearing and facial nerve outcomes, and follow-up data was collected and analyzed. RESULTS: The main symptoms were hearing loss in 26 ELSTs (86.7%) and tinnitus in 17 (56.7%). Twenty-four (80%) ELSTs were in advanced stages (Grade III). The median tumor volume was 6.35 cm3. The median intraoperative blood loss volume was 300 mL. Facial nerve function was well preserved in 21 patients. Among all patients with Grade III tumors, 12 patients underwent tension-free anterior facial nerve rerouting, and 11 patients (91.7%) maintained good facial nerve function postoperatively (HB I and HB II). Only one patient exhibited permanent vocal cord paralysis, and no patients experienced cerebrospinal fluid (CSF) leakage postoperatively. Gross total resection was achieved in 22 patients (73.3%), 5 patients (16.7%) experienced tumor recurrence, and 3 (10%) had residual tumors. CONCLUSIONS: Most ELSTs tend to be diagnosed in the advanced stage. Tension-free anterior facial nerve rerouting could maximally preserve facial nerve function. The intraoperative blood loss volume was associated with tumor size and stage. Tumor recurrence tends to occur at the posterior edge of the petrosal bone, internal auditory canal, and surface of the posterior fossa. Given the relatively high recurrence rate of ELSTs, long-term follow-up is recommended. LEVEL OF EVIDENCE: 4 Laryngoscope, 2024.

Silk Fabrics Modified with Photothermal Phase Change Microcapsules for Personal Thermal Management.

Introduction: With the development of technology, personal heat management has become a focus of attention. Phase change fabrics, as intelligent materials, are expected to be widely used in multiple fields, bringing comfortable, intelligent and convenient living experience. Methods: In this study, miniature phase change microcapsules (MPCM) with n-octadecane as core and poly(methyl methacrylate) as shell were successfully prepared. Using the in-situ reduction property of polydopamine, gold nanoparticles were deposited on the surface of the microcapsules, which retained the heat storage function and imparted photothermal and antibacterial properties. The MPCM with photothermal conversion function was modified on the surface of silk fabric using aqueous polyurethane after verified by comprehensive material characterisation techniques. Results: Under the near infrared light of 808 nm wavelength and 0.134 W/cm² irradiation intensity, the MPCM@PDA@Au modified silk fabrics showed excellent photothermal conversion performance, which could be increased from 25°C to 60°C in 50s. After the light source was cut off, the fabrics showed good heat release ability, with melting enthalpy and crystallisation enthalpy reaching 41.58 J/g and 43.3 J/g, respectively, which were not changed after repeated cycles. After the light source is cut off, the fabric has good heat release ability, and the enthalpy of melting and crystallisation reaches 41.58 J/g and 43.3 J/g, respectively, and the photothermal efficiency remains unchanged after many cycles of use, which proves that it has excellent durability and stability. The antimicrobial test shows that the fabric has significant antibacterial effect on Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). Discussion: MPCM@PDA@Au silk fabrics bring new possibilities for the future of personal thermal management and antimicrobial protection in the field of medical health, outdoor sports and other areas of broad application prospects, heralding the birth of a series of innovative applications and solutions.

A Mixed Methods Examination of Session Planning Among Public Mental Health Therapists.

Session planning is a core activity for implementing evidence-based practices (EBPs), yet it is unknown whether public mental health settings provide the support for therapists to session plan. This two-part study conducted in collaboration with EBP leaders in Philadelphia's public mental health system deployed mixed methods to examine therapists' session planning practices and preferences. In Study 1, 61 public mental health therapists completed an online survey to identify session planning barriers and facilitators, current practices, and desired planning supports. In Study 2, nine therapists who ranked a session planning tool as a top choice support in Study 1 participated in two focus groups to elaborate on their survey responses and provide feedback on three session planning tool prototypes. Study 1 survey respondents cited multi-level barriers and facilitators to session planning. In both closed- and open-ended responses, analyzed descriptively and via content analysis respectively, therapists described wanting more time, lower caseloads, financial incentives for session planning, and additional clinical resources and guidance from trainings, peers, and supervisors to support session planning. Study 2 focus group participants, whose responses were analyzed using content analysis, reiterated the need for these multi-level supports and expressed the need for a "one-stop" database of session planning tools that would be free, easily searchable, and modifiable for varied clinical needs. All three session planning tool prototypes reviewed were acceptable; two were also considered feasible and appropriate. This investigation of an under-studied aspect of the EBP implementation process reveals the need for multi-level session planning supports.

Comparison of Characteristics Between Early-Onset and Late-Onset Severe Preeclampsia: A Retrospective Cohort Study from a Tertiary Hospital in China.

This study aimed to explore the different characteristics between early-onset severe preeclampsia (ESPE) and late-onset severe preeclampsia (LSPE) to improve pregnancy outcomes. We performed a retrospective cohort study between January 2016 and December 2021. Eligible hospitalized pregnant women with severe preeclampsia were assigned into the early-onset or late-onset group, depending on the gestational age at the time of severe preeclampsia onset (< or ≥ 34 gestational weeks, respectively). The clinical characteristics, laboratory results, maternal complications, and fetal and neonatal outcomes were recorded and compared between the two groups. A total of 1,238 pregnant women were included, with 525 in the early-onset group and 713 in the late-onset group. The late-onset group had more cases of gestational diabetes, whereas the early-onset group had a higher blood pressure, showed more proteinuria, had more liver and renal damage, exhibited more serious adverse maternal, fetal, and neonatal outcomes, was more likely to be admitted to the intensive care unit, and required longer hospital stays (all P < 0.05). In addition, the early-onset group had fewer prenatal care appointments and was more often transferred from a primary or secondary care hospital. The logistic regression analysis showed that a weekly weight gain of > 100 g was a risk factor for ESPE and that fewer prenatal care appointments were a risk factor for ESPE in pregnant women with female fetuses. Moreover, logistic regression analysis indicated that nulliparity and gestational diabetes during the current pregnancy were risk factors for LSPE. In conclusion, compared with the women with LSPE, those with ESPE usually had worse maternal, fetal, and neonatal outcomes. More frequent prenatal screening and care should be provided for pregnant women with high-risk factors.

Current research status and future prospects of NLRP3 inflammasome in cardiovascular diseases: a bibliometric and visualization analysis.

Background: Cardiovascular disease (CVD) is a leading cause of global mortality, with atherosclerosis (AS) contributing to its pathological basis. Inflammation plays a critical role in the pathophysiological process of AS, and the NOD-like receptor protein 3 (NLRP3) inflammasome has been extensively studied in this context. This study aimed to analyze the research status of the NLRP3 inflammasome in cardiovascular disease and provide research directions for further exploration in this field. Methods: Using the "Bibliometrix" and "CiteSpace" software, a total of 516 articles were retrieved from the Web of Science (WoS) database published between 2012 and 2023. The search query used the keywords "["CVD" OR "cardiovascular disease"] AND ["NLRP3 inflammasome "OR "NLRP3"]". Visual analysis was performed on authors, countries, institutions, journal sources, keywords, references, and future trends. Results: A total of 516 English articles were retrieved, showing an overall upward trend in annual publication volume with slight fluctuations. China, the United States, and Europe were the countries and regions with the highest number of published articles. Among them, China had the highest article count (170), while the United States had the highest citation count (18,664), centrality score (0.43), and h-index (90), indicating its influential role in this research area. These countries also possessed elite institutions, professional researchers, and high-impact journals, making them leading contributors in this field. The main pathogenic mechanisms of the NLRP3 inflammasome in CVD were identified as "oxidative stress", "pyroptosis", and "inflammation". The most frequently studied signaling pathways included "NF-κB", "IL-1", and "C-reactive protein". The most studied disease types were coronary heart disease, atherosclerosis, metabolic syndrome, and myocardial infarction. Additionally, research on the correlation between cholesterol markers and inflammatory indicators associated with NLRP3 inflammasome in CVD risk assessment has gained significant momentum, with the main mechanism being NLRP3/IL-6/hs-CRP and cholesterol lipoproteins emerging as a major keyword in this context. Conclusion: This study provides valuable insights into the research hotspots and emerging trends of the NLRP3 inflammasome in cardiovascular disease. The findings offer guidance for researchers and scholars in this field and facilitate the exploration of new research directions.

Architecture design and advanced manufacturing of heart-on-a-chip: scaffolds, stimulation and sensors.

Heart-on-a-chip (HoC) has emerged as a highly efficient, cost-effective device for the development of engineered cardiac tissue, facilitating high-throughput testing in drug development and clinical treatment. HoC is primarily used to create a biomimetic microphysiological environment conducive to fostering the maturation of cardiac tissue and to gather information regarding the real-time condition of cardiac tissue. The development of architectural design and advanced manufacturing for these "3S" components, scaffolds, stimulation, and sensors is essential for improving the maturity of cardiac tissue cultivated on-chip, as well as the precision and accuracy of tissue states. In this review, the typical structures and manufacturing technologies of the "3S" components are summarized. The design and manufacturing suggestions for each component are proposed. Furthermore, key challenges and future perspectives of HoC platforms with integrated "3S" components are discussed. Architecture design concepts of scaffolds, stimulation and sensors in chips.

A Bioinspired Membrane with Ultrahigh Li+/Na+ and Li+/K+ Separations Enables Direct Lithium Extraction from Brine.

Membranes with precise Li+/Na+ and Li+/K+ separations are imperative for lithium extraction from brine to address the lithium supply shortage. However, achieving this goal remains a daunting challenge due to the similar valence, chemical properties, and subtle atomic-scale distinctions among these monovalent cations. Herein, inspired by the strict size-sieving effect of biological ion channels, a membrane is presented based on nonporous crystalline materials featuring structurally rigid, dimensionally confined, and long-range ordered ion channels that exclusively permeate naked Li+ but block Na+ and K+. This naked-Li+-sieving behavior not only enables unprecedented Li+/Na+ and Li+/K+ selectivities up to 2707.4 and 5109.8, respectively, even surpassing the state-of-the-art membranes by at least two orders of magnitude, but also demonstrates impressive Li+/Mg2+ and Li+/Ca2+ separation capabilities. Moreover, this bioinspired membrane has to be utilized for creating a one-step lithium extraction strategy from natural brines rich in Na+, K+, and Mg2+ without utilizing chemicals or creating solid waste, and it simultaneously produces hydrogen. This research has proposed a new type of ion-sieving membrane and also provides an envisioning of the design paradigm and development of advanced membranes, ion separation, and lithium extraction.

Heat-induced modulation of flavonoid biosynthesis via a LhMYBC2-Mediated regulatory network in oriental hybrid lily.

Global warming significantly threatens crop production, and adversely affects plant physiology due to rising temperatures. Oriental hybrid lily, an ornamental plant of economic importance, experiences flower color changes in response to elevated temperatures. Anthocyanins belong to a subgroup of flavonoids and are the primary pigments responsible for the coloration of oriental hybrid lily petals. However, the regulatory mechanisms governing flavonoid biosynthesis under high temperature conditions in lilies remain poorly understood. In this study, we revealed the altered metabolite profiles in flavonoid biosynthesis using quasi-targeted metabolomic and transcriptomic analyses. Isoflavonoids accumulate substantially under high temperature conditions, whereas the accumulation of anthocyanin decreases. The expression of the isoflavone reductase gene (LhIFR) and the transcription factor LhMYBC2 were upregulated in response to high temperatures. The LhMYBC2 protein, which belongs to Subgroup 4-AtMYB4, competes with the anthocyanin positive regulator LhMYBA1 for the LhTT8 partner, thereby repressing the formation of a positively regulated transcription complex. Heterologous overexpression of LhMYBC2 in tobacco led to reduced anthocyanin accumulation and increased isoflavonoid accumulation, corroborating its role in inhibiting anthocyanin biosynthesis. This study proposes a regulatory model wherein LhMYBC2 acts as a mediator of flavonoid biosynthesis, influencing the coloration of lily flowers under high-temperature stress. These findings deepen our understanding of the metabolic and transcriptional responses of lily to heat stress and underscore the potential role of LhMYBC2 in mitigating anthocyanin accumulation.

Non-target discovery and risk prediction of per- and polyfluoroalkyl substances (PFAS) and transformation products in wastewater treatment systems.

Wastewater treatment plants (WWTPs) serve as the main destination of many wastes containing per- and polyfluoroalkyl substances (PFAS). Here, we investigated the occurrence and transformation of PFAS and their transformation products (TPs) in wastewater treatment systems using high-resolution mass spectrometry-based target, suspect, and non-target screening approaches. The results revealed the presence of 896 PFAS and TPs in aqueous and sludge phases, of which 687 were assigned confidence levels 1-3 (46 PFAS and 641 TPs). Cyp450 metabolism and environmental microbial degradation were found to be the primary metabolic transformation pathways for PFAS within WWTPs. An estimated 52.3 %, 89.5 %, and 13.6 % of TPs were believed to exhibit persistence, bioaccumulation, and toxicity effects, respectively, with a substantial number of TPs posing potential health risks. Notably, the length of the fluorinated carbon chain in PFAS and TPs was likely associated with increased hazard, primarily due to the influence of biodegradability. Ultimately, two high riskcompounds were identified in the effluent, including one PFAS (Perfluorobutane sulfonic acid) and one enzymatically metabolized TP (23-(Perfluorobutyl)tricosanoic acid@BTM0024_cyp450). It is noteworthy that the toxicity of some TPs exceeded that of their parent compounds. The results from this study underscores the importance of PFAS TPs and associated environmental risks.

Amnioinfusion compared with expectant management in oligohydramnios with intact amnions in the second and early third trimesters.

INTRODUCTION: Treatment of oligohydramnios in the mid-trimester is challenging, because of the high incidence of adverse perinatal outcomes mainly due to bronchopulmonary dysplasia. Antenatal amnioinfusion has been proposed as a possible treatment for oligohydramnios with intact amnions, but there are few relevant studies. This study aimed to evaluate the effectiveness of transabdominal amnioinfusion in the management of oligohydramnios without fetal lethal malformations in the second and early third trimesters. MATERIAL AND METHODS: It is a historical cohort study. A total of 79 patients diagnosed with oligohydramnios at 18-32 weeks gestation were enrolled. In the amnioinfusion group (n = 39), patients received transabdominal amnioinfusion with the assistance of real-time ultrasound guidance. In the expectant group (n = 41), patients were treated with 3000 mL of intravenous isotonic fluids daily. The perioperative complications and perinatal outcomes were analyzed. RESULTS: Compared with the expectant group, the delivery latency was significantly prolonged, and the rate of cesarean delivery was significantly reduced in the amnioinfusion group (p < 0.05). Although the rate of intrauterine fetal death was significantly reduced, the incidence of spontaneous miscarriage, premature rupture of membranes (PROMs), and threatened preterm labor were significantly higher in the amnioinfusion group than in the expectant group (p < 0.05). There was no significant difference in terms of perinatal mortality (28.9% vs. 41.4%, p > 0.05). Multivariate logistic regression revealed that amnioinfusion (odds ratio [OR] 0.162, 95% confidence interval [CI] 0.04-0.61, p = 0.008) and gestational age at diagnosis (OR 0.185, 95% CI 0.04-0.73, p = 0.016) were independently associated with neonatal adverse outcomes. Further subgrouping showed that amnioinfusion significantly reduced the frequency of bronchopulmonary hypoplasia for patients ≤26 weeks (26.7% vs. 75.0%, p = 0.021). The rates of other neonatal complications were similar in both groups. CONCLUSIONS: Amnioinfusion has no significant effect on improving the perinatal mortality of oligohydramnios in the second and early third trimesters. It may lead to a relatively high rate of PROM and spontaneous abortion. However, amnioinfusion may significantly improve the latency period, the rate of cesarean delivery, and neonatal outcomes of oligohydramnios, especially for women ≤26 weeks with high risk of neonatal bronchopulmonary hypoplasia.

Metabolic Labeling and Digital Microfluidic Single-Cell Sequencing for Single Bacterial Genotypic-Phenotypic Analysis.

Accurate assessment of phenotypic and genotypic characteristics of bacteria can facilitate comprehensive cataloguing of all the resistance factors for better understanding of antibiotic resistance. However, current methods primarily focus on individual phenotypic or genotypic profiles across different colonies. Here, a Digital microfluidic-based automated assay for whole-genome sequencing of single-antibiotic-resistant bacteria is reported, enabling Genotypic and Phenotypic Analysis of antibiotic-resistant strains (Digital-GPA). Digital-GPA can efficiently isolate and sequence antibiotic-resistant bacteria illuminated by fluorescent D-amino acid (FDAA)-labeling, producing high-quality single-cell amplified genomes (SAGs). This enables identifications of both minor and major mutations, pinpointing substrains with distinctive resistance mechanisms. Digital-GPA can directly process clinical samples to detect and sequence resistant pathogens without bacterial culture, subsequently provide genetic profiles of antibiotic susceptibility, promising to expedite the analysis of hard-to-culture or slow-growing bacteria. Overall, Digital-GPA opens a new avenue for antibiotic resistance analysis by providing accurate and comprehensive molecular profiles of antibiotic resistance at single-cell resolution.

The gut microbiota-brain axis in neurological disorders.

Previous studies have shown a bidirectional communication between human gut microbiota and the brain, known as the microbiota-gut-brain axis (MGBA). The MGBA influences the host's nervous system development, emotional regulation, and cognitive function through neurotransmitters, immune modulation, and metabolic pathways. Factors like diet, lifestyle, genetics, and environment shape the gut microbiota composition together. Most research have explored how gut microbiota regulates host physiology and its potential in preventing and treating neurological disorders. However, the individual heterogeneity of gut microbiota, strains playing a dominant role in neurological diseases, and the interactions of these microbial metabolites with the central/peripheral nervous systems still need exploration. This review summarizes the potential role of gut microbiota in driving neurodevelopmental disorders (autism spectrum disorder and attention deficit/hyperactivity disorder), neurodegenerative diseases (Alzheimer's and Parkinson's disease), and mood disorders (anxiety and depression) in recent years and discusses the current clinical and preclinical gut microbe-based interventions, including dietary intervention, probiotics, prebiotics, and fecal microbiota transplantation. It also puts forward the current insufficient research on gut microbiota in neurological disorders and provides a framework for further research on neurological disorders.

Effect of Green Light Replacing Some Red and Blue Light on Cucumis melo under Drought Stress.

Light quality not only directly affects the photosynthesis of green plants but also plays an important role in regulating the development and movement of leaf stomata, which is one of the key links for plants to be able to carry out normal growth and photosynthesis. By sensing changes in the light environment, plants actively regulate the expansion pressure of defense cells to change stomatal morphology and regulate the rate of CO2 and water vapor exchange inside and outside the leaf. In this study, Cucumis melo was used as a test material to investigate the mitigation effect of different red, blue, and green light treatments on short-term drought and to analyze its drought-resistant mechanism through transcriptome and metabolome analysis, so as to provide theoretical references for the regulation of stomata in the light environment to improve the water use efficiency. The results of the experiment showed that after 9 days of drought treatment, increasing the percentage of green light in the light quality significantly increased the plant height and fresh weight of the treatment compared to the control (no green light added). The addition of green light resulted in a decrease in leaf stomatal conductance and a decrease in reactive oxygen species (ROS) content, malondialdehyde MDA content, and electrolyte osmolality in the leaves of melon seedlings. It indicated that the addition of green light promoted drought tolerance in melon seedlings. Transcriptome and metabolome measurements of the control group (CK) and the addition of green light treatment (T3) showed that the addition of green light treatment not only effectively regulated the synthesis of abscisic acid (ABA) but also significantly regulated the hormonal pathway in the hormones such as jasmonic acid (JA) and salicylic acid (SA). This study provides a new idea to improve plant drought resistance through light quality regulation.

An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study.

OBJECTIVES: To develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features, and imaging features at multiple semantic levels to predict the prognosis of intracerebral hemorrhage (ICH) patients at 6 months post-onset. MATERIALS AND METHODS: Retrospectively enrolled 222 patients with ICH for Non-contrast Computed Tomography (NCCT) images and clinical data, who were divided into a training cohort (n = 186, medical center 1) and an external testing cohort (n = 36, medical center 2). Following image preprocessing, the entire hematoma region was segmented by two radiologists as the volume of interest (VOI). Pyradiomics algorithm library was utilized to extract 1762 radiomics features, while a deep convolutional neural network (EfficientnetV2-L) was employed to extract 1000 deep learning features. Additionally, radiologists evaluated imaging features. Based on the three different modalities of features mentioned above, the Random Forest (RF) model was trained, resulting in three models (Radiomics Model, Radiomics-Clinical Model, and DL-Radiomics-Clinical Model). The performance and clinical utility of the models were assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), calibration curve, and Decision Curve Analysis (DCA), with AUC compared using the DeLong test. Furthermore, this study employs three methods, Shapley Additive Explanations (SHAP), Grad-CAM, and Guided Grad-CAM, to conduct a multidimensional interpretability analysis of model decisions. RESULTS: The Radiomics-Clinical Model and DL-Radiomics-Clinical Model exhibited relatively good predictive performance, with an AUC of 0.86 [95% Confidence Intervals (CI): 0.71, 0.95; P < 0.01] and 0.89 (95% CI: 0.74, 0.97; P < 0.01), respectively, in the external testing cohort. CONCLUSION: The multimodal explainable AI model proposed in this study can accurately predict the prognosis of ICH. Interpretability methods such as SHAP, Grad-CAM, and Guided Grad-Cam partially address the interpretability limitations of AI models. Integrating multimodal imaging features can effectively improve the performance of the model. CLINICAL RELEVANCE STATEMENT: Predicting the prognosis of patients with ICH is a key objective in emergency care. Accurate and efficient prognostic tools can effectively prevent, manage, and monitor adverse events in ICH patients, maximizing treatment outcomes.