Improved mapping of heavy metals in agricultural soils using machine learning augmented with spatial regionalization indices.

The accurate spatial mapping of heavy metal levels in agricultural soils is crucial for environmental management and food security. However, the inherent limitations of traditional interpolation methods and emerging machine-learning techniques restrict their spatial prediction accuracy. This study aimed to refine the spatial prediction of heavy metal distributions in Guangxi, China, by integrating machine learning models and spatial regionalization indices (SRIs). The results demonstrated that random forest (RF) models incorporating SRIs outperformed artificial neural network and support vector regression models, achieving R2 values exceeding 0.96 for eight heavy metals on the test data. Hierarchical clustering for feature selection further improved the model performance. The optimized RF models accurately predicted the heavy metal distributions in agricultural soils across the province, revealing higher levels in the central-western regions and lower levels in the north and south. Notably, the models identified that 25.78 % of agricultural soils constitute hotspots with multiple co-occurring heavy metals, and over 6.41 million people are exposed to excessive soil heavy metal levels. Our findings provide valuable insights for the development of targeted strategies for soil pollution control and agricultural soil management to safeguard food security and public health.

Effects of repetitive transcranial magnetic stimulation on lower extremity motor function and optimal parameters in stroke patients with different stages of stroke: a systematic evaluation and meta-analysis.

Background: Repetitive transcranial magnetic stimulation (rTMS), as an emerging non-invasive neuromodulation technique, is now widely employed in rehabilitation therapy. The purpose of this paper is to comprehensively summarize existing evidence regarding rTMS intervention for lower limb motor function in patients at different stages of stroke. Methods: A systematic search was conducted to identify randomized controlled trials (RCTs) assessing the efficacy of rTMS for treating lower limb motor dysfunction after stroke. Multiple databases, including China National Knowledge Infrastructure (CNKI), Wanfang Data Knowledge Service Platform, VIP Database, PubMed, Embase, Web of Science, and Cochrane Library, were searched. The search period extended from the inception of the libraries to June 2024. Literature information was extracted, and methodological quality was evaluated using the risk of bias assessment tool in the Cochrane Handbook. Meta-analysis was performed using Stata 17.0 software. Results: Overall, 49 appropriate studies (including 3,558 stroke subjects) were found. Meta-analysis results demonstrated that rTMS effectively improved lower limb motor function across all stages of stroke. The intervention was particularly more effective in patients in the subacute stage than in the acute or chronic stages. Subgroup analysis revealed that, for acute-stage patients, low-frequency stimulation targeting the M1 or DLPFC brain regions on the unaffected side with 20-40 sessions significantly improved FMA-LE scores. In subacute-phase patients, low-frequency stimulation targeting the M1 brain regions on the unaffected side with 18 sessions significantly improved FMA-LE scores. The results demonstrated that HF-rTMS was more effective than LF-rTMS in improving walking speed, with the greatest efficacy observed at 20 sessions. While for enhancing gait balance in stroke patients, LF-rTMS with the best therapeutic effect was observed at a frequency of 20-40 treatments. Conclusion: This study demonstrates the efficacy of rTMS in improving lower limb motor function, balance, and walking speed in stroke patients at various stages. The findings provide a valuable reference for the development of optimized rTMS treatment plans in clinical practice.Systematic review registration: PROSPERO: CRD42023466094.

MVD Variants Identified in a Rare Clinical Variant of Porokeratosis: A Case Report of Disseminated Superficial Porokeratosis (DSP) in a Chinese Patient.

Porokeratosis comprises a diverse range of both hereditary and acquired disorders characterized by clonal hyperproliferation of keratinocytes. These disorders manifest with a variety of clinical presentations but are histologically unified by the presence of the cornoid lamella. In this study, we report an unusual presentation of a rare clinical variant of porokeratosis, namely disseminated superficial porokeratosis, in which mutations in the Mevalonate decarboxylase (MVD) gene have been identified. This finding contributes to the growing understanding of the genetic underpinnings of this complex dermatological condition and may have implications for diagnosis and treatment.

Lachnoclostridium intestinal flora is associated with immunotherapy efficacy in nasopharyngeal carcinoma.

BACKGROUND: Effective biomarkers for assessing anti-PD-1/PD-L1 therapy efficacy in patients with nasopharyngeal carcinoma (NPC) are still lacking. The human gut microbiota has been shown to influence clinical response to anti-PD-1/PD-L1 therapy in many cancers. However, the relationship between the gut microbiota and the efficacy of immunotherapy in patients with nasopharyngeal carcinoma has not been determined. METHODS: We conducted a prospective study in which fecal and blood samples from patients with NPC were subjected to 16S rDNA sequencing and survival analysis. To investigate potential differences in the gut microbiome between these groups and to identify potential biomarkers indicative of immunotherapy efficacy, patients were categorized into two groups according to their clinical response to immunotherapy, the responder group (R group) and the non-responder group (NR group). Progression-free survival (PFS) between these subgroups was analyzed using Kaplan-Meier survival analysis with the log-rank test. Additionally, we performed univariate and multivariate analyses to evaluate prognostic factors. Finally, we carried out non-targeted metabolomics to examine the metabolic effects associated with the identified microbiome. RESULTS: Our 16S rDNA sequencing results showed that the abundance of Lachnoclostridium was higher in the NR group than in the R group (p = 0.003), and alpha diversity analysis showed that the abundance of microbiota in the NR group was higher than that in the R group (p = 0.050). Patients with a lower abundance of Lachnoclostridium had better PFS (p = 0.048). Univariate (p = 0.017) and multivariate analysis (p = 0.040) showed that Lachnoclostridium was a predictor of PFS. Non-targeted metabolomics analysis revealed that Lachnoclostridium affects the efficacy of immunotherapy through the usnic acid. CONCLUSIONS: High abundance of Lachnoclostridium predicts poor prognosis in patients with NPC receiving immunotherapy.

Bipolar disorder and oxidative stress: A bibliometric perspective.

OBJECTIVE: Accumulating evidence indicates that oxidative stress and the disruption of antioxidant defenses play an important role in the neurobiology of bipolar disorder (BD). Studies have found that increased oxidative stress may be associated with cell apoptosis and neuronal damage in BD patients. Hence, this study explored the research field related to BD and oxidative stress from a bibliometrics perspective. METHODS: Literature search and relevant data retrieval based on the Web of Sciences Core Collection (WoSCC). R software (version 4.2.2), VOSviewer software (version 1.6.18), and CiteSpace (version 6.1.6) were used in this bibliometric analysis. RESULTS: A total of 2081 publications related to BD and oxidative stress were published between 1986 and 2024. Bipolar Disorders was the journal that had the most publications in this area (72; 3.46%; IF = 5.9), while the United States (1285; 61.7%) and the University of Toronto (377; 18.1%) were the most productive country and institution, respectively. Apart from "oxidative stress" and "bipolar disorder," the most frequently used keywords were "schizophrenia," "prefrontal cortex," and "nitric oxide." CONCLUSIONS: The growing number of publications related to BD and oxidative stress in recent years highlights the importance of this research field. Hot topics in research related to BD and oxidative stress included animal experiments and molecular mechanisms, psychiatric-related inflammation and biomarkers, neurodegenerative diseases, and metabolism. Furthermore, the biological mechanisms of BD, particularly biomarkers and inflammation, may be the emerging research priority area in the future.

miR-1290 induces endothelial-to-mesenchymal transition and promotes vascular restenosis after angioplasty by targeting FGF2.

BACKGROUND AND AIMS: Endothelial-to-mesenchymal transition (EndMT) is an important reason for restenosis but the underlying mechanisms need to be further explored. Therefore, the purpose of this study is to screen significantly different microRNAs (miRNAs) and assess their functions and downstream pathways. METHODS: This study screened several miRNAs with significant differences between human arterial segments from restenosis patients and healthy volunteers using whole transcriptome resequencing and real-time quantitative reverse transcription PCR (qRT-PCR). We explored the correlation between miR-1290 and EndMT using Western blot, qRT-PCR, Pearson correlation analysis and further functional gain and loss experiments. Subsequently, we identified the direct downstream target of miR-1290 by bioinformatics analysis, RNA pull-down, double Luciferase reporter gene and other functional experiments. Finally, rat carotid artery balloon injury model demonstrated the therapeutic potential of miR-1290 regulator. RESULTS: We screened 129 differentially expressed miRNAs. Among them, miR-1290 levels were significantly higher in restenosis arteries than in healthy arteries, and as expected, EndMT was functionally enhanced with miR-1290 overexpression and comparatively weakened when miR-1290 was knocked down. In addition, fibroblast growth factor-2 (FGF2) was established as the downstream target of miR-1290. Finally, we utilized an animal model and found that low miR-1290 levels could alleviate EndMT and the progression of restenosis. CONCLUSIONS: Our study demonstrated the strong regulatory effects of miR-1290 on EndMT, endometrial hyperplasia and restenosis, which could be useful as biomarker and therapeutic target for stent implantation in patients with arterial occlusive disease of the lower extremities.

Intercepting Dendrite Growth With a Heterogeneous Solid Electrolyte for Long-Life All-Solid-State Lithium Metal Batteries.

The application of lithium metal anode in all-solid-state batteries has the potential to achieve both high energy density and safety performance. However, the presence of serious dendrite issues hinders this potential. Here, the ion transport pathways and orientation of dendrite growth are regulated by utilizing the differences of ionic conductivity in heterogeneous electrolytes. The in situ formed Li-Ge alloy phases from the spontaneous reaction between Li10GeP2S12 and the attracted dendrites greatly enhance the ability to resist dendrite growth. As an outcome, the heterogeneous electrolyte achieves a high critical current density of 2.1 mA cm-2 and long-term stable symmetrical battery operation (0.3 mA cm-2 for 17 000 h and 1.0 mA cm-2 for 2000 h). Besides, due to the superior interfacial stability and low interface impedance between the heterogeneous electrolyte and lithium anode, the Li||LiNi0.8Co0.1Mn0.1O2 full battery exhibits great cycling stability (80.5% after 500 cycles at 1.0 mA cm-2) and rate performance (125.4 mAh g at 2.0 mA cm-2). This work provides a unique strategy of interface regulation via heterogeneous electrolytes design, offering insights into the development of state-of the-art all-solid-state batteries.

Debate and Dilemmas Regarding Generative AI in Mental Health Care: Scoping Review.

BACKGROUND: Mental disorders have ranked among the top 10 prevalent causes of burden on a global scale. Generative artificial intelligence (GAI) has emerged as a promising and innovative technological advancement that has significant potential in the field of mental health care. Nevertheless, there is a scarcity of research dedicated to examining and understanding the application landscape of GAI within this domain. OBJECTIVE: This review aims to inform the current state of GAI knowledge and identify its key uses in the mental health domain by consolidating relevant literature. METHODS: Records were searched within 8 reputable sources including Web of Science, PubMed, IEEE Xplore, medRxiv, bioRxiv, Google Scholar, CNKI and Wanfang databases between 2013 and 2023. Our focus was on original, empirical research with either English or Chinese publications that use GAI technologies to benefit mental health. For an exhaustive search, we also checked the studies cited by relevant literature. Two reviewers were responsible for the data selection process, and all the extracted data were synthesized and summarized for brief and in-depth analyses depending on the GAI approaches used (traditional retrieval and rule-based techniques vs advanced GAI techniques). RESULTS: In this review of 144 articles, 44 (30.6%) met the inclusion criteria for detailed analysis. Six key uses of advanced GAI emerged: mental disorder detection, counseling support, therapeutic application, clinical training, clinical decision-making support, and goal-driven optimization. Advanced GAI systems have been mainly focused on therapeutic applications (n=19, 43%) and counseling support (n=13, 30%), with clinical training being the least common. Most studies (n=28, 64%) focused broadly on mental health, while specific conditions such as anxiety (n=1, 2%), bipolar disorder (n=2, 5%), eating disorders (n=1, 2%), posttraumatic stress disorder (n=2, 5%), and schizophrenia (n=1, 2%) received limited attention. Despite prevalent use, the efficacy of ChatGPT in the detection of mental disorders remains insufficient. In addition, 100 articles on traditional GAI approaches were found, indicating diverse areas where advanced GAI could enhance mental health care. CONCLUSIONS: This study provides a comprehensive overview of the use of GAI in mental health care, which serves as a valuable guide for future research, practical applications, and policy development in this domain. While GAI demonstrates promise in augmenting mental health care services, its inherent limitations emphasize its role as a supplementary tool rather than a replacement for trained mental health providers. A conscientious and ethical integration of GAI techniques is necessary, ensuring a balanced approach that maximizes benefits while mitigating potential challenges in mental health care practices.

A novel sp3 carbon allotrope with 4+5+6+7+8 odd-even ring.

In this study, we report a novel monoclinic phase of carbon that contains 4+5+6+7+8 member rings in P21/m symmetry, identified by applying the stochastic surface walking method combined with high dimensional neural network potentials. We demonstrate that this phase possesses lower energy than graphite above 21.5 GPa. The phonon spectra show that this structure is stable under ambient pressure. This phase is a super hard material with a shear hardness as high as 81.9 GPa while it possesses an indirect band gap of 3.16 eV. The energy barrier of graphite to the Y phase is 0.27 eV, slightly higher than that of the hexagonal diamond (0.21 eV) in a similar phase transition mechanism. Two types of thermodynamically stable interfaces can be formed with the hexagonal diamond (HD), namely (001)Y//(100)HD, [100]Y//[010]HD and (001)Y//(001)HD, [010]Y//[001]HD. Although the discrete bulk Y phase is hard to synthesize, a faulted structure between HD is possible because of the well-matched interface between Y and HD. Our work shows that the Y phase may be formed in some special conditions and enhances our understanding of the formation of novel carbon allotropes.

MAVS Cys508 palmitoylation promotes its aggregation on the mitochondrial outer membrane and antiviral innate immunity.

Cysteine palmitoylation or S-palmitoylation catalyzed by the ZDHHC family of acyltransferases regulates the biological function of numerous mammalian proteins as well as viral proteins. However, understanding of the role of S-palmitoylation in antiviral immunity against RNA viruses remains very limited. The adaptor protein MAVS forms functionally essential prion-like aggregates upon activation by viral RNA-sensing RIG-I-like receptors. Here, we identify that MAVS, a C-terminal tail-anchored mitochondrial outer membrane protein, is S-palmitoylated by ZDHHC7 at Cys508, a residue adjacent to the tail-anchor transmembrane helix. Using superresolution microscopy and other biochemical techniques, we found that the mitochondrial localization of MAVS at resting state mainly depends on its transmembrane tail-anchor, without regulation by Cys508 S-palmitoylation. However, upon viral infection, MAVS S-palmitoylation stabilizes its aggregation on the mitochondrial outer membrane and thus promotes subsequent propagation of antiviral signaling. We further show that inhibition of MAVS S-palmitoylation increases the host susceptibility to RNA virus infection, highlighting the importance of S-palmitoylation in the antiviral innate immunity. Also, our results indicate ZDHHC7 as a potential therapeutic target for MAVS-related autoimmune diseases.

Oral Piwi-Interacting RNA Delivery Mediated by Green Tea-Derived Exosome-Like Nanovesicles for the Treatment of Aortic Dissection.

Aortic dissection (AD) is a severe cardiovascular disease necessitating active therapeutic strategies for early intervention and prevention. Nucleic acid drugs, known for their potent molecule-targeting therapeutic properties, offer potential for genetic suppression of AD. Piwi-interacting RNAs, a class of small RNAs, hold promise for managing cardiovascular diseases. Limited research on these RNAs and AD exists. This study demonstrates that an antagomir targeting heart-apoptosis-associated piRNA (HAAPIR) effectively regulates vascular remodeling, mitigating AD occurrence and progression through the myocyte enhancer factor 2D (Mef2D) and matrix metallopeptidase 9 (MMP9) pathways. Green tea-derived plant exosome-like nanovesicles (PELNs) are used for oral administration of antagomir. The antagomir-HAAPIR-nanovesicle complex, after purification and optimization, exhibits a high packing rate, while the antagomir is resistant to enzyme digestion. Administered to mice, the complex targets the aortic lesion, reducing AD incidence and improving survival. Moreover, MMP9 and Mef2D expression decrease significantly, inhibiting the phenotypic conversion of human aortic smooth muscle cells. PELNs encapsulate the antagomir-HAAPIR complex, maintaining stability, mediating transport into the bloodstream, and delivering Piwi-interacting RNAs to AD sites. Thus, HAAPIR is a potential target for persistent clinical AD prevention and treatment, and nanovesicle-encapsulated nucleic acids offer a promising cardiovascular disease treatment, providing insights for other therapeutic targets.

Selective recognition and enrichment of C70 over C60 using an anthracene-based nanotube.

Selective recognition and enrichment of fullerenes (e.g., C60 and C70) remains challenging due to the same diameter and geometrical similarity. Herein, we report a hexagonal anthracene-based nanotube (1) through a one-pot Suzuki-Miyaura cross-coupling reaction. With anthracene-based side walls and pyridine linkers, 1 features a nano-scale tubular cavity measuring 1.2 nm in diameter and 0.9 nm in depth, along with pH-responsive properties. Interestingly, the electron-rich 1 shows high binding affinity (K a ≈ 106 M-1) and selectivity (K s ≈ 140) to C70 over C60 in toluene, resulting from their different contribution of π-π interactions with the host. The protonation of 1 simultaneously alters the electronic properties within the nanotube, resulting in the release of the fullerene guests. Lastly, the selective recognition and pH stimuli-responsive properties of the nanotube have been utilized to enrich C70 from its low-content mixtures of fullerenes in chloroform.

Identification of male schizophrenia patients using brain morphology based on machine learning algorithms.

BACKGROUND: Schizophrenia is a severe psychiatric disease, and its prevalence is higher. However, diagnosis of early-stage schizophrenia is still considered a challenging task. AIM: To employ brain morphological features and machine learning method to differentiate male individuals with schizophrenia from healthy controls. METHODS: The least absolute shrinkage and selection operator and t tests were applied to select important features from structural magnetic resonance images as input features for classification. Four commonly used machine learning algorithms, the general linear model, random forest (RF), k-nearest neighbors, and support vector machine algorithms, were used to develop the classification models. The performance of the classification models was evaluated according to the area under the receiver operating characteristic curve (AUC). RESULTS: A total of 8 important features with significant differences between groups were considered as input features for the establishment of classification models based on the four machine learning algorithms. Compared to other machine learning algorithms, RF yielded better performance in the discrimination of male schizophrenic individuals from healthy controls, with an AUC of 0.886. CONCLUSION: Our research suggests that brain morphological features can be used to improve the early diagnosis of schizophrenia in male patients.

Machine learning-based prediction of cadmium pollution in topsoil and identification of critical driving factors in a mining area.

Mining activities have resulted in a substantial accumulation of cadmium (Cd) in agricultural soils, particularly in southern China. Long-term Cd exposure can cause plant growth inhibition and various diseases. Rapid identification of the extent of soil Cd pollution and its driving factors are essential for soil management and risk assessment. However, traditional geostatistical methods are difficult to simulate the complex nonlinear relationships between soil Cd and potential features. In this study, sequential extraction and hotspot analyses indicated that Cd accumulation increased significantly near mining sites and exhibited high mobility. The concentration of Cd was estimated using three machine learning models based on 3169 topsoil samples, seven quantitative variables (soil pH, Fe, Ca, Mn, TOC, Al/Si and ba value) and three quantitative variables (soil parent rock, terrain and soil type). The random forest model achieved marginally better performance than the other models, with an R2 of 0.78. Importance analysis revealed that soil pH and Ca and Mn contents were the most significant factors affecting Cd accumulation and migration. Conversely, due to the essence of controlling Cd migration being soil property, soil type, terrain, and soil parent materials had little impact on the spatial distribution of soil Cd under the influence of mining activities. Our results provide a better understanding of the geochemical behavior of soil Cd in mining areas, which could be helpful for environmental management departments in controlling the diffusion of Cd pollution and capturing key targets for soil remediation.

A network analysis of the interrelationships between depression, anxiety, insomnia and quality of life among fire service recruits.

Background: Research on the mental health and quality of life (hereafter QOL) among fire service recruits after the end of the COVID-19 restrictions is lacking. This study explored the network structure of depression, anxiety and insomnia, and their interconnections with QOL among fire service recruits in the post-COVID-19 era. Methods: This cross-sectional study used a consecutive sampling of fire service recruits across China. We measured the severity of depression, anxiety and insomnia symptoms, and overall QOL using the nine-item Patient Health Questionnaire (PHQ-9), seven-item Generalized Anxiety Disorder scale (GAD-7), Insomnia Severity Index (ISI) questionnaire, and World Health Organization Quality of Life-brief version (WHOQOL-BREF), respectively. We estimated the most central symptoms using the centrality index of expected influence (EI), and the symptoms connecting depression, anxiety and insomnia symptoms using bridge EI. Results: In total, 1,560 fire service recruits participated in the study. The prevalence of depression (PHQ-9 ≥ 5) was 15.2% (95% CI: 13.5-17.1%), while the prevalence of anxiety (GAD-7 ≥ 5) was 11.2% (95% CI: 9.6-12.8%). GAD4 ("Trouble relaxing") had the highest EI in the whole network model, followed by ISI5 ("Interference with daytime functioning") and GAD6 ("Irritability"). In contrast, PHQ4 ("Fatigue") had the highest bridge EI values in the network, followed by GAD4 ("Trouble relaxing") and ISI5 ("Interference with daytime functioning"). Additionally, ISI4 "Sleep dissatisfaction" (average edge weight = -1.335), which was the central symptom with the highest intensity value, had the strongest negative correlation with QOL. Conclusion: Depression and anxiety were important mental health issues to address among fire service recruits in the post-COVID-19 era in China. Targeting central and bridge symptoms identified in network analysis could help address depression and anxiety among fire service recruits in the post-COVID-19 era.

On-line measurement of COD and nitrate in water against stochastic background interference based on ultraviolet-visible spectroscopy and physics-informed multi-task learning.

Traditional ultraviolet-visible spectroscopic quantitative analytical methods face challenges in simultaneous and long-term accurate measurement of chemical oxygen demand (COD) and nitrate due to spectral overlap and the interference from stochastic background caused by turbidity and chromaticity in water. Addressing these limitations, a compact dual optical path spectrum detection sensor is introduced, and a novel ultraviolet-visible spectroscopic quantitative analysis model based on physics-informed multi-task learning (PI-MTL) is designed. Incorporating a physics-informed block, the PI-MTL model integrates pre-existing physical knowledge for enhanced feature extraction specific to each task. A multi-task loss wrapper strategy is also employed, facilitating comprehensive loss evaluation and adaptation to stochastic backgrounds. This novel approach significantly outperforms conventional models in COD and nitrate measurement under stochastic background interference, achieving impressive prediction R2 values of 0.941 for COD and 0.9575 for nitrate, while reducing root mean squared error (RMSE) by 60.89 % for COD and 77.3 % for nitrate in comparison to the conventional chemometric model partial least squares regression (PLSR), and by 30.59 % and 65.96 %, respectively, in comparison to a benchmark convolutional neural network (CNN) model. The promising results emphasize its potential as a spectroscopic instrument designed for online multi-parameter water quality monitoring against stochastic background interference, enabling long-term accurate measurement of COD and nitrate levels.

Developing BioNavi for Hybrid Retrosynthesis Planning.

Illuminating synthetic pathways is essential for producing valuable chemicals, such as bioactive molecules. Chemical and biological syntheses are crucial, and their integration often leads to more efficient and sustainable pathways. Despite the rapid development of retrosynthesis models, few of them consider both chemical and biological syntheses, hindering the pathway design for high-value chemicals. Here, we propose BioNavi by innovating multitask learning and reaction templates into the deep learning-driven model to design hybrid synthesis pathways in a more interpretable manner. BioNavi outperforms existing approaches on different data sets, achieving a 75% hit rate in replicating reported biosynthetic pathways and displaying superior ability in designing hybrid synthesis pathways. Additional case studies further illustrate the potential application of BioNavi in a de novo pathway design. The enhanced web server (http://biopathnavi.qmclab.com/bionavi/) simplifies input operations and implements step-by-step exploration according to user experience. We show that BioNavi is a handy navigator for designing synthetic pathways for various chemicals.