The origin of methyl group in methanogen-mediated mercury methylation: From the Wolfe cycle.

Methylmercury (MeHg) is a bioaccumulating neurotoxin mainly produced by anaerobic microorganisms, with methanogen being one of the important methylators. A critical aspect for understanding the mechanism for microbial mercury (Hg) methylation is the origin of the methyl group. However, the origin of methyl group in methanogen-mediated Hg methylation remains unclear. This study aims to identify the source of methyl group for MeHg synthesis in methanogens. Our study revealed that Hg methylation in Methanospirillum hungatei JF-1 is closely related to methanogenesis process, according to the results of proteomic study and substrate limitation study. Next, we proved that nearly all methyl group in MeHg derives from the Wolfe cycle in this species, rather than the previously demonstrated acetyl-coenzyme A pathway, based on the results of 13C labeling study. We then proposed the Wolfe cycle-dependent Hg methylation mechanism in this species. Further genome analyses and 13C labeling experiments indicated that the involvement of the Wolfe cycle in Hg methylation is probably a universal feature among Hg-methylating methanogens. These findings reveal a unique Hg methylation mechanism in methanogens. Our study broadens the carbon substrates and controlling factors for MeHg synthesis in the environment, which can inform the prediction of MeHg production potential and remediation strategies for MeHg contamination.

Transcriptome analysis of Sesuvium portulacastrum L. uncovers key genes and pathways involved in root formation in response to low-temperature stress.

Sesuvium portulacastrum L., a perennial facultative halophyte, is extensively distributed across tropical and subtropical coastal regions. Its limited cold tolerance significantly impacts both the productivity and the geographical distribution of this species in higher-latitude areas. In this study, we employed RNA-Seq technology to delineate the transcriptomic alterations in Sesuvium plants exposed to low temperatures, thus advancing our comprehension of the molecular underpinnings of this physiological adaptation and root formation. Our findings demonstrated differential expression of 10,805, 16,389, and 10,503 genes in the low versus moderate temperature (LT vs. MT), moderate versus high temperature (MT vs. HT), and low versus high temperature (LT vs. HT) comparative analyses, respectively. Notably, the gene categories "structural molecule activity", "ribosome biogenesis", and "ribosome" were particularly enriched among the LT vs. HT-specific differentially expressed genes (DEGs). When synthesizing the insights from these three comparative studies, the principal pathways associated with the cold response mechanism were identified as "carbon fixation in photosynthetic organisms", "starch and sucrose metabolism", "plant hormone signal transduction", "glycolysis/gluconeogenesis", and "photosynthesis". In addition, we elucidated the involvement of auxin signaling pathways, adventitious root formation (ARF), lateral root formation (LRF), and novel genes associated with shoot system development in root formation. Subsequently, we constructed a network diagram to investigate the interplay between hormone levels and pivotal genes, thereby clarifying the regulatory pathways of plant root formation under low-temperature stress and isolating key genes instrumental in root development. This study has provided critical insights into the molecular mechanisms that facilitate the adaptation to cold stress and root formation in S. portulacastrum.

Inorganic polyphosphate and ion transport across biological membranes.

Inorganic polyphosphate (polyP) is widely recognized for playing important roles and processes involved in energy and phosphate storage, regulation of gene expression, and calcium signaling. The less well-known role of polyP is as a direct mediator of ion transport across biological membranes. Here, we will briefly summarize current knowledge of the molecular mechanisms of how polyP can be involved in membrane ion transport. We discuss three types of mechanisms that might involve polyP: (1) formation of non-protein channel complex that includes calcium, polyP, and polyhydroxybutyrate (PHB); (2) modulation of the channel activity of PHBlated protein channels; and (3) direct effects of polyP on the function of the voltage-gated ion channels in the process that do not involve PHB.

Integrative lncRNA, circRNA, and mRNA analysis reveals expression profiles of six forensic body fluids/tissue.

RNAs have attracted much attention in forensic body fluid/tissue identification (BFID) due to their tissue-specific expression characteristics. Among RNAs, long RNAs (e.g., mRNA) have a higher probability of containing more polymorphic sites that can be used to assign the specific donor of the body fluid/tissue. However, few studies have characterized their overall profiles in forensic science. In this study, we sequenced the transcriptomes of 30 samples from venous blood, menstrual blood, semen, saliva, vaginal secretion, and skin tissue, obtaining a comprehensive picture of mRNA, lncRNA, and circRNA profiles. A total of 90,305 mRNAs, 102,906 lncRNAs (including 19,549 novel lncRNAs), and 40,204 circRNAs were detected. RNA type distribution, length distribution, and expression distribution were presented according to their annotation and expression level, and many novel body fluid/tissue-specific RNA markers were identified. Furthermore, the cognate relations among the three RNAs were analyzed according to gene annotations. Finally, SNPs and InDels from RNA transcripts were genotyped, and 21,611 multi-SNP and 4,471 multi-InDel transcriptomic microhaplotypes (tMHs) were identified. These results provide a comprehensive understanding of transcriptome profiles, which could provide new avenues for tracing the origin of the body fluid/tissue and identifying an individual.

Inference of forensic body fluids/tissues based on mitochondrial DNA copy number: a preliminary study.

The inference of body fluids and tissues is critical in reconstructing crime scenes and inferring criminal behaviors. Nevertheless, present methods are incompatible with conventional DNA genotyping, and additional testing might result in excessive consumption of forensic scene materials. This study aims to investigate the feasibility of distinguishing common body fluids/tissues through the difference in mitochondrial DNA copy number (mtDNAcn). Four types of body fluids/tissues were analyzed in this study - hair, saliva, semen, and skeletal muscle. MtDNAcn was estimated by dividing the read counts of mitochondrial DNA to that of nuclear DNA (RRmt/nu). Results indicated that there were significant differences in RRmt/nu between different body fluids/tissues. Specifically, hair samples exhibited the highest RRmt/nu (log10RRmt/nu: 4.3 ± 0.28), while semen samples showed the lowest RRmt/nu (log10RRmt/nu: -0.1 ± 0.28). RRmt/nu values for DNA samples without extraction were notably higher (approximately 2.9 times) than those obtained after extraction. However, no significant difference in RRmt/nu was observed between various age and gender groups. Hierarchical clustering and Kmeans clustering analyses showed that body fluids/tissues of the same type clustered closely to each other and could be inferred with high accuracy. In conclusion, this study demonstrated that the simultaneous detection of nuclear and mitochondrial DNA made it possible to perform conventional DNA analyses and body fluid/tissue inference at the same time, thus killing two birds with one stone. Furthermore, mtDNAcn has the potential to serve as a novel and promising biomarker for the identification of body fluids/tissues.

CNSMolGen: A Bidirectional Recurrent Neural Network-Based Generative Model for De Novo Central Nervous System Drug Design.

Central nervous system (CNS) drugs have had a significant impact on treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models have shown great potential for accelerating drug discovery and improving efficacy. However, specific applications of these techniques in CNS drug discovery have not been widely reported. In this study, we developed the CNSMolGen model, which uses a framework of bidirectional recurrent neural networks (Bi-RNNs) for de novo molecular design of CNS drugs. Results showed that the pretrained model was able to generate more than 90% of completely new molecular structures, which possessed the properties of CNS drug molecules and were synthesizable. In addition, transfer learning was performed on small data sets with specific biological activities to evaluate the potential application of the model for CNS drug optimization. Here, we used drugs against the classical CNS disease target serotonin transporter (SERT) as a fine-tuned data set and generated a focused database against the target protein. The potential biological activities of the generated molecules were verified by using the physics-based induced-fit docking study. The success of this model demonstrates its potential in CNS drug design and optimization, which provides a new impetus for future CNS drug development.

Molecular Mechanism-Driven Discovery of Novel Small Molecule Inhibitors against Drug-Resistant SARS-CoV-2 Mpro Variants.

Under the selective pressure of nirmatrelvir, a peptidomimetic covalent drug targeting SARS-CoV-2 Mpro, various drug-resistant mutations on Mpro have been acquired in vitro. Among the mutations, L50F and E166V, along with the combination of L50F and E166V, are particularly representative and pose considerable obstacles to the effective treatment of COVID-19. Our previous study identified NMI-001 and NMI-002 as novel nonpeptide inhibitors that target SARS-CoV-2 Mpro, possessing unique scaffolds and binding modes different from those of nirmatrelvir. In view of these findings, we proposed a drug design strategy aimed at rapidly identifying inhibitors that can combat mutation-induced drug resistance. Initially, molecular dynamics (MD) simulation was employed to investigate the binding mechanisms of NMI-001 and NMI-002 against the three drug-resistant mutants (Mpro_L50F, Mpro_E166V, and Mpro_L50F+E166V). Then, we conducted two phases of high-throughput virtual screening. In the first phase, NMI-001 served as a template to perform scaffold hopping-based similarity search in a library of 15,742,661 compounds. In the second phase, 968 compounds exhibiting similarity to NMI-001 were evaluated via molecular docking and MD simulations. Six compounds that may be effective against at least one mutant were identified, and five compounds were procured for conducting in vitro assays. Finally, the compound Z1557501297 (NMI-003) exhibiting inhibitory effects against the E166V (IC50 = 27.81 ± 2.65 µM) and L50F+E166V (IC50 = 8.78 ± 0.74 µM) mutants was discovered. The binding modes referring to NMI-003-Mpro_E166V and NMI-003-Mpro_L50F+E166V were further elucidated at the atomic level. In summary, NMI-003 reported herein is the first compound with activity against E166V and L50F+E166V, which provides a good starting point to design novel antiviral drugs for the treatment of drug-resistant SARS-CoV-2.

Structure-Based Discovery of the SARS-CoV-2 Main Protease Noncovalent Inhibitors from Traditional Chinese Medicine.

Traditional Chinese medicine (TCM) has been extensively employed for the treatment of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there is demand for discovering more SARS-CoV-2 Mpro inhibitors with diverse scaffolds to optimize anti-SARS-CoV-2 lead compounds. In this study, comprehensive in silico and in vitro assays were utilized to determine the potential inhibitors from TCM compounds against SARS-CoV-2 Mpro, which is an important therapeutic target for SARS-CoV-2. The ensemble docking analysis of 18263 TCM compounds against 15 SARS-CoV-2 Mpro conformations identified 19 TCM compounds as promising candidates. Further in vitro testing validated three compounds as inhibitors of SARS-CoV-2 Mpro and showed IC50 values of 4.64 ± 0.11, 7.56 ± 0.78, and 11.16 ± 0.26 µM, with EC50 values of 12.25 ± 1.68, 15.58 ± 0.77, and 29.32 ± 1.25 µM, respectively. Molecular dynamics (MD) simulations indicated that the three complexes remained stable over the last 100 ns of production run. An analysis of the binding mode revealed that the active compounds occupy different subsites (S1, S2, S3, and S4) of the active site of SARS-CoV-2 Mpro via specific poses through noncovalent interactions with key amino acids (e.g., HIS 41, ASN 142, GLY 143, MET 165, GLU 166, or GLN 189). Overall, this study provides evidence indicating that the three natural products obtained from TCM could be further used for anti-COVID-19 research, justifying the investigation of Chinese herbal medicinal ingredients as bioactive constituents for therapeutic targets.

Discovery of a Novel and Potent LCK Inhibitor for Leukemia Treatment via Deep Learning and Molecular Docking.

The lymphocyte-specific protein tyrosine kinase (LCK) plays a crucial role in both T-cell development and activation. Dysregulation of LCK signaling has been demonstrated to drive the oncogenesis of T-cell acute lymphoblastic leukemia (T-ALL), thus providing a therapeutic target for leukemia treatment. In this study, we introduced a sophisticated virtual screening strategy combined with biological evaluations to discover potent LCK inhibitors. Our initial approach involved utilizing the PLANET algorithm to assess and contrast various scoring methodologies suitable for LCK inhibitor screening. After effectively evaluating PLANET, we progressed to devise a virtual screening workflow that synergistically combines the strengths of PLANET with the capabilities of Schrödinger's suite. This integrative strategy led to the efficient identification of four potential LCK inhibitors. Among them, compound 1232030-35-1 stood out as the most promising candidate with an IC50 of 0.43 nM. Further in vitro bioassays revealed that 1232030-35-1 exhibited robust antiproliferative effects on T-ALL cells, which was attributed to its ability to suppress the phosphorylations of key molecules in the LCK signaling pathway. More importantly, 1232030-35-1 treatment demonstrated profound in vivo antileukemia efficacy in a human T-ALL xenograft model. In addition, complementary molecular dynamics simulations provided deeper insight into the binding kinetics between 1232030-35-1 and LCK, highlighting the formation of a hydrogen bond with Met319. Collectively, our study established a robust and effective screening strategy that integrates AI-driven and conventional methodologies for the identification of LCK inhibitors, positioning 1232030-35-1 as a highly promising and novel drug-like candidate for potential applications in treating T-ALL.

Magnetic materials as adsorbents for the pre-concentration and separation of active ingredients from herbal medicine.

Herbal medicine (HM) is crucial in disease management and contains complex compounds with few active pharmacological ingredients, presenting challenges in quality control of raw materials and formulations. Effective separation, identification, and analysis of active components are vital for HM efficacy. Traditional methods like liquid-liquid extraction and solid-phase extraction are time-consuming and environmentally concerning, with limitations such as sorbent issues, pressure, and clogging. Magnetic solid-phase extraction uses magnetic sorbents for targeted analyte separation and enrichment, offering rapid, pressure-free separation. However, inorganic magnetic particles' aggregation and oxidation, as well as lack of selectivity, have led to the use of various coatings and modifications to enhance specificity and selectivity for complex herbal samples. This review delves into magnetic composites in HM pretreatment, specifically focusing on encapsulated or modified magnetic nanoparticles and materials like silica, ionic liquids, graphene family derivatives, carbon nanotubes, metal-organic frameworks, covalent organic frameworks, and molecularly imprinted polymers.

Controversy between biopsy and risk in children with proteinuria: is there a paradigm war?

BACKGROUND: Proteinuria is a prevalent symptom of pediatric nephrology, while kidney biopsy remains the gold standard for kidney tissue analysis, and it is currently controversial. We report the rare case that the mutation in the AMN gene was considered to cause chronically isolated proteinuria and also suggest that renal biopsy should be chosen with caution in children with chronic isolated non-nephrotic levels of proteinuria and that genetic testing may be feasible for the early precise diagnosis. CASE PRESENTATION: A 35-month-old boy presented with excessive urine foaming for more than half a month; his proteinuria was considered non-nephrotic range and urine protein electrophoresis was suggestive of mixed proteinuria; other than that, the investigations are non-specific. Given the child's chronic isolated proteinuria and good renal function, we chose to refine the genetic test rather than a renal biopsy; a compound heterozygous variant was found in the AMN gene of this child which was caused by a point mutation in the father, and a partial chromosomal deletion in the mother. CONCLUSIONS: Cubilin(encoded by CUBN), amnionless(encoded by AMN), and megalin form a multiligand receptor complex; CUBN or AMN gene variants have been implicated as a hereditary cause of megaloblastic anemia, proteinuria, and neurological impairment. In the past few decades, chronic isolated proteinuria caused by CUBN gene variants is benign, non-progressive, and has normal renal function. However, the child is the first reported case of isolated proteinuria of AMN gene mutation, indicating that the earlier diagnostic genetic sequencing in an otherwise well, not nephrotic proteinuria child may be a convenient, cost-effective, and harmless option, challenging the traditional paradigm.

Hydrazone chemistry-mediated CRISPR/Cas12a system for bacterial analysis.

In this study, a hydrazone chemistry-mediated clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated protein 12a (Cas12a) system has been proposed for the fist time and constructed. In our system, hydrazone chemistry is designed and employed to accelerate the formation of a whole activation strand by taking advantage of the proximity effect induced by complementary base pairing, thus activating the CRISPR/Cas12a system quickly and efficiently. Moreover, the introduction of hydrazone chemistry can improve the specificity of the CRISPR/Cas12a system, allowing it to effectively distinguish single-base mismatches. The established system has been further applied to analyze Pseudomonas aeruginosa by specific recognition of the probe strand with a characteristic fragment in 16S rDNA to release the hydrazine group-modified activation strand. The method shows a wide linear range from 3.8 × 102 colony-forming units (CFU)/ml to 3.8 × 106 CFU/ml, with the lowest detection limit of 24 CFU/ml. Therefore, the introduction of hydrazone chemistry may also broaden the application of the CRISPR/Cas12a system.

A nomogram based on clinical multivariate factors predicts delayed cure after microvascular decompression for hemifacial spasm.

BACKGROUND: The course of disease after microvascular decompression (MVD) in patients with hemifacial spasm (HFS) is variable. The purpose of this study was to develop and validate a nomogram to predict the probability of delayed cure after microvascular decompression in patients with hemifacial spasms based on clinical multivariate factors. METHODS: A retrospective data collection was performed on 290 patients with HFS undergoing MVD at our center from January 2017 to January 2022. The patients were randomly assigned to the training cohort (n = 232) and validation cohort (n = 58) at a ratio of 8:2. Retrospective analysis was performed of information on clinical, radiological, and intraoperative findings and clinical outcomes. Univariate and multivariate analyses were performed in the training cohort, and a nomogram was constructed using a stepwise logistic regression approach. The receiver operating characteristic (ROC) was calculated to evaluate the reliability of the nomogram model. Decision curve analysis (DCA) was used to assess the clinical application value of the nomogram model. RESULTS: In the training cohorts, 73 patients (73/232) had a delayed cure. In the validation cohorts, 18 patients (18/58) had a delayed cure. We developed a novel nomogram model to predict the risk of delayed cure after MVD in HFS patients based on the presence of vertebral artery compression, venous compression, absence of LSR, degree of facial nerve indentation, degree of neurovascular compression, and internal auditory canal vascular looThe area under the curve (AUC) of the nomogram model was 0.9483 in the training cohort and 0.9382 in the validation cohort. The calibration curve showed good correspondence between the predicted and actual probabilities in the training and validation groups. The decision curve showed that the nomogram model had good performance in clinical applications. CONCLUSIONS: We developed and validated a preoperative and intraoperative multivariate factors nomogram to predict the possibility of delayed cure after MVD in HFS patients, which may help clinicians in the comprehensive management of HFS.

A nomogram based on radiomics and clinical information to predict prognosis in percutaneous balloon compression for the treatment of trigeminal neuralgia.

OBJECTIVE: To develop a clinical-radiomics nomogram based on clinical information and radiomics features to predict the prognosis of percutaneous balloon compression (PBC) for the treatment of trigeminal neuralgia (TN). METHODS: The retrospective study involved clinical data from 149 TN patients undergoing PBC at Zhongnan Hospital, Wuhan University from January 2018 to January 2022. The free open-source software 3D Slicer was used to extract all radiomic features from the intraoperative X-ray balloon region. The relationship between clinical information and TN prognosis was analyzed by univariate logistic analysis and multivariate logistic analysis. Using R software, the optimal radiomics features were selected using the least absolute shrinkage and selection operator (Lasso) algorithm. A prediction model was constructed based on the clinical information and radiomic features, and a nomogram was visualized. The performance of the clinical radiomics nomogram in predicting the prognosis of PBC in TN treatment was evaluated using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). RESULTS: A total of 149 patients were eventually included. The clinical factors influencing the prognosis of TN in univariate analysis were compression severity score and TN type. The lasso algorithm Max-Relevance and Min-Redundancy(mRMR) was used to select two predictors from 13 morphology-related radiomics features, including elongation and surface-volume ratio. A total of 4 predictors were used to construct a prediction model and nomogram. The AUC was 0.886(95% confidence interval (CI), 0.75 to 0.96), indicating that the model's good predictive ability. DCA demonstrated the nomogram's high clinical applicability. CONCLUSION: Clinical-radiomics nomogram constructed by combining clinical information and morphology-related radiomics features have good potential in predicting the prognosis of TN for PBC treatment. However, this needs to be further studied and validated in several independent external patient populations.

A Novel Mitochondrial-Related Gene Signature for the Prediction of Prognosis and Therapeutic Efficacy in Lower-Grade Glioma.

Lower-grade glioma (LGG) is a common primary brain tumor with a highly heterogeneous clinical presentation, and its prognosis cannot be accurately predicted by current histopathology. It has been found that mitochondria play an important role in hypoxia, angiogenesis, and energy metabolism in glioma, and mitochondrial function may have an important impact on LGG prognosis. The goal of this study was to develop a novel prognostic model based on Mitochondrial-related genes (MRGs). We first analyzed the somatic alterations profiles of MRGs in patients with LGG and found that somatic alterations were common in LGG and correlated with prognosis. Using RNA-seq data from TCGA and CGGA, 12 prognosis-related MRGs were identified to construct a mitochondrial activation score (MiAS) model by combining univariate regression and LASSO regression analysis. The model and nomogram were evaluated using the area under the ROC curve with AUC = 0.910. The model was closely correlated with the clinical characteristics of LGG patients and performed well in predicting the prognosis of LGG patients with significantly shorter overall survival (OS) time in the high-MiAS group. GSVA and GSEA results showed that oxidative stress, pro-cancer, and immune-related pathways were significantly enriched in the high-MiAS group. CIBERSORT results showed that MiAS was significantly associated with immune cell infiltration in LGG. Macrophage M1 and follicular helper T cells had increased infiltration in the high-MiAS group. TIDE predicted a better immunotherapy outcome in patients in the low-MiAS group. Finally, using data from the CTRPv2 and GDSC2 datasets to assess chemotherapy response in LGG, it was predicted that the chemotherapeutic agents AZD6482, MG-132, and PLX-4720 might be potential agents for patients in the high-MiAS group of LGG. In addition, we performed in vitro experiments and found that knockdown of OCIAD2 expression reduced the abilities of glioma cells to proliferate, migrate, and invade. In contrast, overexpression of OCIAD2 enhanced these abilities of glioma cells. This study found that MRGs were correlated with LGG patient prognosis, which is expected to provide new treatment strategies for LGG patients with different MiAS.

Mapping the Evolution of Digital Health Research: Bibliometric Overview of Research Hotspots, Trends, and Collaboration of Publications in JMIR (1999-2024).

BACKGROUND: While bibliometric studies of individual journals have been conducted, to the best of our knowledge, bibliometric mapping has not yet been utilized to analyze the literature published by the Journal of Medical Internet Research (JMIR). OBJECTIVE: In celebration of the journal's 25th anniversary, this study aimed to review the entire collection of JMIR publications from 1999 to 2024 and provide a comprehensive overview of the main publication characteristics. METHODS: This study included papers published in JMIR during the 25-year period from 1999 to 2024. The data were analyzed using CiteSpace, VOSviewer, and the "Bibliometrix" package in R. Through descriptive bibliometrics, we examined the dynamics and trend patterns of JMIR literature production and identified the most prolific authors, papers, institutions, and countries. Bibliometric maps were used to visualize the content of published articles and to identify the most prominent research terms and topics, along with their evolution. A bibliometric network map was constructed to determine the hot research topics over the past 25 years. RESULTS: This study revealed positive trends in literature production, with both the total number of publications and the average number of citations increasing over the years. And the global COVID-19 pandemic induced an explosive rise in the number of publications in JMIR. The most productive institutions were predominantly from the United States, which ranked highest in successful publications within the journal. The editor-in-chief of JMIR was identified as a pioneer in this field. The thematic analysis indicated that the most prolific topics aligned with the primary aims and scope of the journal. Currently and in the foreseeable future, the main themes of JMIR include "artificial intelligence," "patient empowerment," and "victimization." CONCLUSIONS: This bibliometric study highlighted significant contributions to digital health by identifying key research trends, themes, influential authors, and collaborations. The findings underscore the necessity to enhance publications from developing countries, improve gender diversity among authors, and expand the range of research topics explored in the journal.

Characterizing the emission trends and pollution evolution patterns during the transition period following COVID-19 at an industrial megacity of central China.

After the resumption of work and production following the COVID-19 pandemic, many cities entered a "transition phase", characterized by the gradual recovery of emission levels from various sources. Although the overall PM2.5 emission trends have recovered, the specific changes in different sources of PM2.5 remain unclear. Here, we investigated the changes in source contributions and the evolution pattern of pollution episodes (PE) in Wuhan during the "transition period" and compared them with the same period during the COVID-19 lockdown. We found that vehicle emissions, industrial processes, and road dust exhibited significant recoveries during the transition period, increasing by 5.4%, 4.8%, and 3.9%, respectively, during the PE. As primary emissions increased, secondary formation slightly declined, but it still played a predominant role (accounting for 39.1∼ 43.0% of secondary nitrate). The reduction in industrial activities was partially offset by residential burning. The evolution characteristics of PE exhibited significant differences between the two periods, with PM2.5 concentration persisting at a high level during the transition period. The differences in the evolution patterns of the two periods were also reflected in their change rates at each stage, which mostly depend on the pre-PE concentration level. The transition period shows a significantly higher value (8.4 µg m-3 h-1) compared with the lockdown period, almost double the amount. In addition to local emissions, regional transport should be a key consideration in pollution mitigation strategies, especially in areas adjacent to Wuhan. Our study quantifies the variations in sources between the two periods, providing valuable insights for optimizing environmental planning to achieve established goals.

From inflammation to pyroptosis: Understanding the consequences of cadmium exposure in chicken liver cells.

Hepatotoxicity is frequently observed following acute cadmium (Cd) exposure in chicken. Oxidative stress and subsequent inflammation are regarded as the main reasons for cadmium-induced liver injury. NOD-like receptor (NLR) family pyrin domain-containing 3 (NLRP3) inflammasome-induced pyroptosis is involved in various inflammatory diseases, including liver injury. Poultry are more susceptible to harmful effects of heavy metals. However, the mechanism of cadmium-induced liver injury in chicken is still elusive. In this study, the effect of cadmium on chicken liver cells and the underlying mechanisms were investigated. The results showed mitochondria was damaged and excessive reactive oxygen species (ROS) were generated in chicken liver cell line LMH after cadmium exposure. Furthermore, cadmium-induced NLRP3 inflammasome activation and the cell membrane rupture indicated LMH cells pyroptosis. The ROS scavengers, acetylcysteine (NAC) and Mito-TEMPO prevented pyroptosis in LMH cells, suggesting that ROS were responsible for the activation of the NLRP3 inflammasome induced by cadmium. Additionally, anti-oxidative transcription factor Nrf2 was inhibited after cadmium exposure, explaining the excessive ROS generation. In summary, our study showed that cadmium leads to ROS generation by inducing mitochondrial damage and inhibiting Nrf2 activity, which promotes NLRP3 inflammasome activation and eventually induces pyroptosis in LMH cells.

Bridge-layered decompression technique for vertebral artery-involved hemifacial spasm: technical note.

BACKGROUND: Hemifacial spasm (HFS) is most effectively treated with microvascular decompression (MVD). However, there are certain challenges in performing MVD for HFS when the vertebral artery (VA) is involved in compressing the facial nerve (VA-involved). This study aimed to introduce a "bridge-layered" decompression technique for treating patients with VA-involved HFS and to evaluate its efficacy and safety to treat patients with HFS. METHODS: A single-center retrospective analysis was conducted on the clinical data of 62 patients with VA-involved HFS. The tortuous trunk of VA was lifted by a multi-point "bridge" decompression technique to avoid excessive traction of the cerebellum and reduce the risk of damage to the facial-acoustic nerve complex. To fully decompress all the responsible vessels, the branch vessels of VA were then isolated using the "layered" decompression technique. RESULTS: Among the 62 patients, 59 patients were cured immediately after the surgery, two patients were delayed cured after two months, and one had occasional facial muscle twitching after the surgery. Patients were followed up for an average of 19.5 months. The long-term follow-up results showed that all patients had no recurrence of HFS during the follow-up period, and no patients developed hearing loss, facial paralysis, or other permanent neurological damage complications. Only two patients developed tinnitus after the surgery. CONCLUSION: The "bridge-layered" decompression technique could effectively treat VA-involved HFS with satisfactory safety and a low risk of hearing loss. The technique could be used as a reference for decompression surgery for VA-involved HFS.

Granite Extraction Based on the SDGSAT-1 Satellite Thermal Infrared Spectrometer Imagery.

Earth observation by remote sensing plays a crucial role in granite extraction, and many current studies use thermal infrared data from sensors such as ASTER. The challenge lies in the low spatial resolution of these satellites, hindering precise rock type identification. A breakthrough emerges with the Thermal Infrared Spectrometer (TIS) on the Sustainable Development Science Satellite 1 (SDGSAT-1) launched by the Chinese Academy of Sciences. With an exceptional 30 m spatial resolution, SDGSAT-1 TIS opens avenues for accurate granite extraction using remote sensing. This study, exemplified in Xinjiang's Karamay region, introduces the BR-ISauvola method, leveraging SDGSAT-1 TIS data. The approach combines band ratio with adaptive k-value selection using local grayscale statistical features for Sauvola thresholding. Focused on large-scale granite extraction, results show F1 scores above 70% for Otsu, Sauvola, and BR-ISauvola. Notably, BR-ISauvola achieves the highest accuracy at 82.11%, surpassing Otsu and Sauvola by 9.62% and 0.34%, respectively. This underscores the potential of SDGSAT-1 TIS data as a valuable resource for granite extraction. The proposed method efficiently utilizes spectral information, presenting a novel approach for rapid granite extraction using remote sensing TIS imagery, even in scenarios with low spectral resolution and a single data source.