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.

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.

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.

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.

Carrier-Free Photodynamic Bioregulators Inhibiting Lactic Acid Efflux Combined with Immune Checkpoint Blockade for Triple-Negative Breast Cancer Immunotherapy.

Abnormal tumor metabolism creates a complex tumor immune microenvironment that plays a dominant role in the metastasis of triple-negative breast cancer (TNBC). TNBC is insensitive to immune checkpoint blockade (ICB) therapy because of insufficient cytotoxic T lymphocyte (CTL) infiltration and a hyper-lactic acid-suppressive immune microenvironment caused by abnormal glycolysis. Herein, we propose an amplified strategy based on lactic acid regulation to reprogram the immunosuppressive tumor microenvironment (ITM) and combine it with ICB therapy to achieve enhanced antitumor immunotherapy effects. Specifically, we constructed CASN, a carrier-free photodynamic bioregulator, through the self-assembly of the photosensitizer Chlorin e6 and monocarboxylate transporter 1 (MCT1) inhibitor AZD3965. CASN exhibited a uniform structure, good stability, and drug accumulation at the tumor site. CASN-mediated photodynamic therapy following laser irradiation inhibited primary tumor growth and induced immunogenic cell death. Furthermore, CASN reduced lactic acid-mediated regulatory T cell generation and M2 tumor-associated macrophage polarization by blocking MCT1-mediated lactic acid efflux to attenuate immune suppression, inducing the recruitment and activation of CTLs. Ultimately, CASN-mediated immunopotentiation combined with ICB therapy considerably strengthened tumor immunotherapy and effectively inhibited tumor growth and metastasis of TNBC. This synergistic amplification strategy overcomes the limitations of an acidic ITM and presents a potential clinical treatment option for metastatic tumors.

Risk Factors of Poststroke Cognitive Impairment: A Meta-Analysis.

OBJECTIVE: To explore the potential risk factors of post stroke cognitive impairment (PSCI) by conducting a meta-analysis. METHODS: Literature search was performed in databases (PubMed, Embase, Web of Science, CNKI) using keywords of PSCI. Cochrane ROB tool was adopted for evaluating the quality of the included studies. Afterwards, data was independently extracted by 2 investigators. Heterogeneity was quantified across studies by Chi-squared-based Q statistic test and I2 statistic. The random-effects model or fixed-effects model was employed to compute the pooled estimates depends on whether the heterogeneity was significant (I2 > 50% or P < .05) or not. Publication bias was evaluated by the funnel plot and Egger's test. Sensitivity analysis was accomplished through eliminating studies 1 at a time to evaluate the stability of the pooled estimates. RESULTS: 23 high-quality studies with 13322 patients were included. Compared with patients with no cognitive impairment, PSCI was more likely to develop in the elderly (pooled MD = 3.58, 95% CI = [1.82, 5.34]), female (pooled RR = 1.23, 95% CI = [1.07, 1.41]), or less-educated (pooled MD = -1.63, 95% CI = [-2.96, -.31]) patients with a history of hypertension (pooled RR = 1.07, 95% CI = [1.03, 1.11]), diabetes mellitus (pooled RR = 1.10, 95% CI = [1.03, 1.17]), atrial fibrillation (pooled RR = 1.38, 95% CI = [1.10, 1.74]), or stroke (pooled RR = 1.36, 95% CI = [1.09, 1.70]). Smoking did not affect the development of PSCI in patients (pooled RR = .96, 95% CI = [.78, 1.19]). Ischemic heart disease and region represented the sources of significant heterogeneity across studies. The pooled estimates were robust, and no publication bias was seen. CONCLUSION: Age, gender, education, hypertension, diabetes mellitus, atrial fibrillation, and stroke were the risk factors of PSCI. Controlling these risk factors can help prevent PSCI.

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.

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.

Polyamine Anabolism Promotes Chemotherapy-Induced Breast Cancer Stem Cell Enrichment.

Breast cancer patients may initially benefit from cytotoxic chemotherapy but experience treatment resistance and relapse. Chemoresistant breast cancer stem cells (BCSCs) play a pivotal role in cancer recurrence and metastasis, however, identification and eradication of BCSC population in patients are challenging. Here, an mRNA-based BCSC signature is developed using machine learning strategy to evaluate cancer stemness in primary breast cancer patient samples. Using the BCSC signature, a critical role of polyamine anabolism in the regulation of chemotherapy-induced BCSC enrichment, is elucidated. Mechanistically, two key polyamine anabolic enzymes, ODC1 and SRM, are directly activated by transcription factor HIF-1 in response to chemotherapy. Genetic inhibition of HIF-1-controlled polyamine anabolism blocks chemotherapy-induced BCSC enrichment in vitro and in xenograft mice. A novel specific HIF-1 inhibitor britannin is identified through a natural compound library screening, and demonstrate that coadministration of britannin efficiently inhibits chemotherapy-induced HIF-1 transcriptional activity, ODC1 and SRM expression, polyamine levels, and BCSC enrichment in vitro and in xenograft and autochthonous mouse models. The findings demonstrate the key role of polyamine anabolism in BCSC regulation and provide a new strategy for breast cancer treatment.

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.

MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma.

Objectives: To investigate the value of interpretable machine learning model and nomogram based on clinical factors, MRI imaging features, and radiomic features to predict Ki-67 expression in primary central nervous system lymphomas (PCNSL). Materials and methods: MRI images and clinical information of 92 PCNSL patients were retrospectively collected, which were divided into 53 cases in the training set and 39 cases in the external validation set according to different medical centers. A 3D brain tumor segmentation model was trained based on nnU-NetV2, and two prediction models, interpretable Random Forest (RF) incorporating the SHapley Additive exPlanations (SHAP) method and nomogram based on multivariate logistic regression, were proposed for the task of Ki-67 expression status prediction. Results: The mean dice Similarity Coefficient (DSC) score of the 3D segmentation model on the validation set was 0.85. On the Ki-67 expression prediction task, the AUC of the interpretable RF model on the validation set was 0.84 (95% CI:0.81, 0.86; p < 0.001), which was a 3% improvement compared to the AUC of the nomogram. The Delong test showed that the z statistic for the difference between the two models was 1.901, corresponding to a p value of 0.057. In addition, SHAP analysis showed that the Rad-Score made a significant contribution to the model decision. Conclusion: In this study, we developed a 3D brain tumor segmentation model and used an interpretable machine learning model and nomogram for preoperative prediction of Ki-67 expression status in PCNSL patients, which improved the prediction of this medical task. Clinical relevance statement: Ki-67 represents the degree of active cell proliferation and is an important prognostic parameter associated with clinical outcomes. Non-invasive and accurate prediction of Ki-67 expression level preoperatively plays an important role in targeting treatment selection and patient stratification management for PCNSL thereby improving prognosis.

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.

Fibroinase plays a vital role in silk gland degeneration by regulating autophagy and apoptosis in the silkworm, Bombyx mori.

The silkworm is an incredibly valuable insect that produces silk through its silk gland. Within this organ, Fibroinase has been identified and named due to its ability to fibroin degradation. The expression of Fibroinase in the silk gland significantly increases during the larval-pupal stage, which might be associated with the degeneration of the silk gland. In this study, Fibroinase was overexpressed and knocked down specifically both in the middle and posterior silk glands, respectively, using transgenic technology. The investigation of silk gland development in these transgenic silkworms showed that Fibroinase plays a direct role in accelerating silk gland degeneration. The staining analyses performed in the silk glands of transgenic silkworms suggest that Fibroinase is involved in the processes of autophagy and apoptosis during silk gland degeneration. Further experiments demonstrated that Fibroinase, acting as a lysosomal regulator, negatively regulates autophagy via the mTOR (mechanistic target of rapamycin) pathway. Moreover, during apoptosis, Fibroinase could activate Caspase3 by increasing the activity of BmCaspase1, ultimately accelerating the apoptosis process. These findings enhance our understanding of the physiological role of Fibroinase in promoting silk gland degeneration, which plays a role in breaking down proteins in the silk gland and coordinating the regulation of autophagy and apoptosis.

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.

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.

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.

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.