Comparison of Combined Dissipation Behaviors and Dietary Risk Assessments of Thiamethoxam, Bifenthrin, Dinotefuran, and Their Mixtures in Tea.

In the tea-planting process, insecticides are commonly combined, potentially prolonging the pre-harvest interval and heightening the risk of dietary exposure. This study focused on three frequently used insecticides in tea cultivation: thiamethoxam, bifenthrin, and dinotefuran, aiming to investigate their dissipation behaviors and associated dietary risks upon individual and simultaneous application. The dissipation kinetics of thiamethoxam, bifenthrin, and dinotefuran were successfully characterized by first-order kinetics, yielding respective half-lives of 5.44, 9.81, and 10.16 days. Upon joint application, the dissipation half-lives of thiamethoxam and bifenthrin were notably prolonged compared with their individual applications, resulting in final concentrations after 28 days that were correspondingly elevated by 1.41 and 1.29 times. Assessment of the dietary intake risk revealed that the chronic and acute risk quotients associated with thiamethoxam and bifenthrin escalated by 1.44-1.59 times following their combined application. Although dietary risks associated with Tianmuhu white tea, as determined by the exposure assessment model, were deemed acceptable, the cumulative risks stemming from pesticide mixtures across various dietary sources warrant attention. Molecular docking analyses further unveiled that thiamethoxam and bifenthrin competitively bound to glutathione S-transferase (GST) at amino acid residues, notably at the 76th GLU and the 25th PHE, pivotal in the metabolism and absorption of exogenous substances. Moreover, the interactions between P-glycoprotein and pesticides during transport and absorption were likely to influence dissipation behaviors post-joint application. This research offers valuable insights and data support for optimizing joint pesticide application strategies and assessing risks associated with typical pesticides used in tea cultivation.

RanBALL: An Ensemble Random Projection Model for Identifying Subtypes of B-cell Acute Lymphoblastic Leukemia.

As the most common pediatric malignancy, B-cell acute lymphoblastic leukemia (B-ALL) has multiple distinct subtypes characterized by recurrent and sporadic somatic and germline genetic alterations. Identification of B-ALL subtypes can facilitate risk stratification and enable tailored therapeutic approaches. Existing methods for B-ALL subtyping primarily depend on immunophenotypic, cytogenetic and genomic analyses, which would be costly, complicated, and laborious in clinical practice applications. To overcome these challenges, we present RanBALL (an Ensemble Random Projection-Based Model for Identifying B-Cell Acute Lymphoblastic Leukemia Subtypes), an accurate and cost-effective model for B-ALL subtype identification based on transcriptomic profiling only. RanBALL leverages random projection (RP) to construct an ensemble of dimension-reduced multi-class support vector machine (SVM) classifiers for B-ALL subtyping. Results based on 100 times 5-fold cross validation tests for >1700 B-ALL patients demonstrated that the proposed model achieved an accuracy of 93.35%, indicating promising prediction capabilities of RanBALL for B-ALL subtyping. The high accuracies of RanBALL suggested that our model could effectively capture underlying patterns of transcriptomic profiling for accurate B-ALL subtype identification. We believe RanBALL will facilitate the discovery of B-ALL subtype-specific marker genes and therapeutic targets, and eventually have consequential positive impacts on downstream risk stratification and tailored treatment design.

WIMOAD: Weighted Integration of Multi-Omics data for Alzheimer's Disease (AD) Diagnosis.

As the most common subtype of dementia, Alzheimer's disease (AD) is characterized by a progressive decline in cognitive functions, especially in memory, thinking, and reasoning ability. Early diagnosis and interventions enable the implementation of measures to reduce or slow further regression of the disease, preventing individuals from severe brain function decline. The current framework of AD diagnosis depends on A/T/(N) biomarkers detection from cerebrospinal fluid or brain imaging data, which is invasive and expensive during the data acquisition process. Moreover, the pathophysiological changes of AD accumulate in amino acids, metabolism, neuroinflammation, etc., resulting in heterogeneity in newly registered patients. Recently, next generation sequencing (NGS) technologies have found to be a non-invasive, efficient and less-costly alternative on AD screening. However, most of existing studies rely on single omics only. To address these concerns, we introduce WIMOAD, a weighted integration of multi-omics data for AD diagnosis. WIMOAD synergistically leverages specialized classifiers for patients' paired gene expression and methylation data for multi-stage classification. The resulting scores were then stacked with MLP-based meta-models for performance improvement. The prediction results of two distinct meta-models were integrated with optimized weights for the final decision-making of the model, providing higher performance than using single omics only. Remarkably, WIMOAD achieves significantly higher performance than using single omics alone in the classification tasks. The model's overall performance also outperformed most existing approaches, highlighting its ability to effectively discern intricate patterns in multi-omics data and their correlations with clinical diagnosis results. In addition, WIMOAD also stands out as a biologically interpretable model by leveraging the SHapley Additive exPlanations (SHAP) to elucidate the contributions of each gene from each omics to the model output. We believe WIMOAD is a very promising tool for accurate AD diagnosis and effective biomarker discovery across different progression stages, which eventually will have consequential impacts on early treatment intervention and personalized therapy design on AD.

Pain and immune function in patients undergoing gastric cancer surgery following stellate ganglion block with total intravenous anesthesia.

BACKGROUND: Stellate ganglion block is a commonly used sympathetic nerve block technique that restores the balance of the sympathetic and vagal nervous systems of the body and inhibits sympathetic nerve activity. AIM: To analyze the effect of a stellate ganglion block combined with total diploma intravenous anesthesia on postoperative pain and immune function in patients undergoing laparoscopic radical gastric cancer (GC) surgery to provide a reference basis for the formulation of anesthesia protocols for radical GC surgery. METHODS: This study included 112 patients who underwent laparoscopic radical surgery for GC between January 2022 and March 2024. There was no restriction on sex. The patient grouping method used was a digital random table method, and the number of cases in each group was 56. The control group was administered total intravenous anesthesia, and the observation group compounded the stellate ganglion block according to the total intravenous anesthesia protocol. Postoperative hemodynamics, pain levels, and immune indices were compared between the groups. RESULTS: The heart rate and mean arterial pressure in the observation group after intubation were lower than those in the control group (P < 0.05). Pain levels were compared between the two groups at 2 hours, 12 hours, 24 hours, and 48 hours after surgery (P > 0.05). The number of CD3+, CD4+, and CD4+/CD8+ cells at the end of surgery was higher in the observation group than in the control group, and the number of CD8+ cells was lower in the observation group than in the control group (P < 0.05). There were no significant differences between the two groups in terms of propofol dosage, awakening time, extubation time, or postoperative adverse reactions (P > 0.05). CONCLUSION: The application of a stellate ganglion block combined with total intravenous anesthesia had no significant effect on postoperative pain levels in patients undergoing laparoscopic radical GC surgery. However, it can safely reduce the effect of surgery on the immune function of patients and is worth applying in clinical practice.

Histidine phosphatase-ferroptosis crosstalk modulation for efficient hepatocellular carcinoma treatment.

Altering the mechanisms of tumor cell death and overcoming the limitations of traditional chemotherapy is pivotal to contemporary tumor treatment. Inducing ferroptosis, while circumventing safety concerns associated with ferrous vectors, through nonferrous ferroptosis is a promising but underexplored frontier in cancer therapy. Histidine phosphatase (LHPP) has emerged as a novel therapeutic target in treating hepatocellular carcinoma (HCC), but the precise mechanism of LHPP against HCC remains unclear. Herein, we explore the effects of upregulating LHPP expression on ferroptosis and tumor immunogenicity induction by simply delivering a miRNA-363-5p inhibitor (miR-363-5pi) via a previously optimized gemcitabine-oleic acid (GOA) prodrug. Efficient miRNA encapsulation was achieved through hydrogen bonding at an optimized GOA/miRNA molar feed ratio of 250:1, affording spherical nanoparticles with a uniform hydrodynamic size of 147.1 nm and a negative potential of -21.5 mV. The mechanism of this LHPP-ferroptosis crosstalk is disclosed to be an inhibited phosphorylation of the PI3K/Akt pathway, leading to a remarkable tumor inhibition rate of 88.2% in nude mice bearing Bel-7402 tumor xenografts via a combination of LHPP-triggered nonferrous ferroptosis and GOA-induced chemotherapy. The biocompatibility of GOA/miR-363-5pi is strongly supported by their non-hematologic toxicity and insignificant organ damage. In addition, the tumor immunogenic activation potential of GOA/miR-363-5pi was finally explored. Overall, this study is the first work that elucidates the precise mechanism of LHPP for treating HCC via ferroptosis induction and achieves the transformation of chemotherapy and gene therapy into ferroptosis activation with tumor cell immunogenicity, which lays a new therapeutic foundation for the clinical treatment of HCC.

Construction and validation of a prognostic model based on immune-metabolic-related genes in oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC), a significant type of head and neck cancer, has witnessed increasing incidence and mortality rates. Immune-related genes (IRGs) and metabolic-related genes (MRGs) play essential roles in the pathogenesis, metastasis, and progression of OSCC. This study exploited data from The Cancer Genome Atlas (TCGA) to identify IRGs and MRGs related to OSCC through differential analysis. Univariate Cox analysis was utilized to determine immune-metabolic-related genes (IMRGs) associated with patient prognosis. A prognostic model for OSCC was constructed using Lasso-Cox regression and subsequently validated with datasets from the Gene Expression Omnibus (GEO). Non-Negative Matrix Factorization (NMF) clustering identified three molecular subtypes of OSCC, among which the C2 subtype showed better overall survival (OS) and progression-free survival (PFS). A prognostic model based on nine IMRGs was developed to categorize OSCC patients into high- and low-risk groups, with the low-risk group demonstrating significantly longer OS in both training and testing cohorts. The model showed strong predictive capabilities, and the risk score served as an independent prognostic factor. Additionally, expression levels of programmed death 1 (PD1) and cytotoxic T-lymphocyte-associated antigen 4 (CTLA4) differed between the risk groups. Gene Set Enrichment Analysis (GSEA) indicated distinct enriched pathways between high-risk and low-risk groups, highlighting the crucial roles of immune and metabolic processes in OSCC. The nine IMRGs prognostic model presented excellent predictive performance and has potential for clinical application.

Research progress of traditional Chinese medicine compound "Chaihu Shugan Powder" in the treatment of premenstrual syndrome.

BACKGROUND: This paper aims to conduct a comprehensive and insightful review and analysis of the potential targets and corresponding pathways of Chaihu Shugan Powder (CSP) for the treatment of premenstrual syndrome (PMS) using a network pharmacology approach. The review will encompass traditional applications, active ingredients of Chinese medicines, clinical applications, pharmacological mechanisms, and active ingredients. METHODS: The active ingredients, pharmacological mechanisms, and clinical applications of the herbal ingredients in the CSP formulation were summarized by searching the literature, and the main signaling pathways of the CSP formulation for the treatment of PMS were identified by network pharmacological studies. RESULTS: CSP is a representative traditional Chinese medicine formula known for its liver detoxification properties and its effectiveness in alleviating depression. It is also recognized as one of the most widely used formulas for treating PMS. In this study, we systematically summarized the active ingredients and pharmacological mechanisms of the 7 traditional Chinese medicine components present in CSP. Through network pharmacology analysis, we identified 75 common targets of CSP relevant to the treatment of PMS. These targets were predominantly concentrated within 17 specific signaling pathways, elucidating the potential molecular mechanisms underlying CSP's therapeutic effects on PMS. CONCLUSION: In this paper, we have reviewed CSP and PMS, investigated the potential targets and corresponding pathways of CSP for the treatment of PMS, and systematically summarized the active ingredients and pharmacological mechanisms of 7 herbal components. In addition, 17 pathways of CSP for PMS were identified for future research and clinical application. However, the specific mechanism of action of CSP for the treatment of PMS is only based on literature and online pharmacological studies, and no basic or clinical experiments have been conducted. In addition, CSP has many components with complex and varied interactions, and the effects of certain compounds may be overlooked. Based on the present findings, it is beneficial to further explore the mechanism of action of the new effector compounds and the prospect of their application in basic research and clinical trials. In conclusion, the revelation of new effector compounds and mechanisms of action is conducive to the further clinical application of CSP, the discovery of new targets for PMS, and the modernization of Chinese medicine.

The Complete Mitochondrial Genomes of Penthe kochi (Coleoptera: Tetratomidae) with Its Phylogenetic Implications.

To explore the mitogenome characteristics of Tetratomidae and the phylogenetic position of this family in Tenebrionoidea, the mitogenome of Penthe kochi Maran, 1940 was sequenced, annotated, and analyzed. The P. kochi mitogenome is consistent with Tenebrionoidea species in gene length, genomic organization, codon usage, and secondary structures of transfer genes (tRNAs). Most protein-coding genes (PCGs) originate with a typical ATN start codon, except nad1 and nad3, which start with TTG. In total, 10 PCGs are terminated with complete stop codon TAA and TAG, while cox1, cox2, and nad 4 contain an incomplete stop codon T-. Among the 13 PCGs, nad2 (Pi = 0.282) has the most diverse nucleotide composition, and cox2 is the most conserved gene with the lowest value (Pi = 0.154). The Ka/Ks ratio of cox1 (0.076) and cox2 (0.124) has a lower value. All the tRNAs can be folded in a typical clover-leaf secondary structure, except trnS1, which lacked a dihydrouridine arm. And phylogenetic analyses were performed based on 13 PCGs using the Bayesian inference (BI) method. The results showed that the clade of Tenebrionoidea was well separated from the outgroups, and Tetratomidae and Mycetophagidae were not well resolved. Phylogenetic analyses with more mitogenome samplings are needed to resolve the phylogeny of Tenebrionoidea.

A review of artificial intelligence-based brain age estimation and its applications for related diseases.

The study of brain age has emerged over the past decade, aiming to estimate a person's age based on brain imaging scans. Ideally, predicted brain age should match chronological age in healthy individuals. However, brain structure and function change in the presence of brain-related diseases. Consequently, brain age also changes in affected individuals, making the brain age gap (BAG)-the difference between brain age and chronological age-a potential biomarker for brain health, early screening, and identifying age-related cognitive decline and disorders. With the recent successes of artificial intelligence in healthcare, it is essential to track the latest advancements and highlight promising directions. This review paper presents recent machine learning techniques used in brain age estimation (BAE) studies. Typically, BAE models involve developing a machine learning regression model to capture age-related variations in brain structure from imaging scans of healthy individuals and automatically predict brain age for new subjects. The process also involves estimating BAG as a measure of brain health. While we discuss recent clinical applications of BAE methods, we also review studies of biological age that can be integrated into BAE research. Finally, we point out the current limitations of BAE's studies.

[Analysis of ACADVL gene variant in a Chinese pedigree affected with Very-long-chain acl-CoA dehydrogenase deficiency].

OBJECTIVE: To carry out genetic testing on a child diagnosed with Very-long-chain acyl-CoA dehydrogenase deficiency (VLADD) in order to provide a basis for genetic counseling and prenatal diagnosis for his family. METHODS: Whole exome sequencing was performed for the proband. Candidate variant sites in the ACADVL gene were verified by Sanger sequencing, and their pathogenicity was predicted based on the guidelines from the American College of Medical Genetics and Genomics (ACMG). Prenatal diagnosis was performed on the fetus upon subsequent pregnancy. This study was approved by the Luoyang Maternal and Child Health Care Hospital (Ethics No. ). RESULTS: The proband was found to harbor compound heterozygous variants of the ACADVL gene, namely c.1532G>A and 1827+2_1827+12del, which were inherited from his mother and father, and classified as likely pathogenic and pathogenic, respectively. By combining the clinical manifestations of the proband and the results of blood tandem mass spectrometry and genetic testing, the child was ultimately diagnosed as cardiomyopathy type VLADD. Prenatal diagnosis showed that the fetus has carried the same compound heterozygous variants, and the couple had opted to terminate the pregnancy. CONCLUSION: The c.1532G>A/1827+2_1827+12del compound heterozygous variants of the ACADVL gene probably underlay the pathogenesis of VLADD in this pedigree. The discovery of the 1827+2_1827+12del variant has enriched the mutational spectrum of the ACADVL gene.

[Genetic analysis of a fetus with Coffin-Siris syndrome 2 due to a novel variant of ARID1A gene].

OBJECTIVE: To explore the genetic etiology of a fetus with Coffin-Siris syndrome (CSS). METHODS: A fetus with abnormal ultrasound findings detected at Luoyang Maternal and Child Health Care Hospital in July 2023 was selected as the study subject. Clinical data were analyzed retrospectively. Whole exome sequencing was carried out on fetal tissue and parental peripheral blood samples, and candidate variant was verified by Sanger sequencing and pathogenicity analysis. This study was approved by the Luoyang Maternal and Child Health Care Hospital (Ethics No. LYFY-YCCZ-2023011). RESULTS: Color Doppler ultrasound at 16+ gestational weeks revealed bilateral ventriculomegaly and cerebellar hypoplasia in the fetus. Trio-WES found that the fetus has harbored a heterozygous c.553C>T (p.Gln185Ter) variant of the ARID1A gene, which was verified by Sanger sequencing to have a de novo origin. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the c.553C>T (p.Gln185Ter) variant of the ARID1A gene was classified as pathogenic (PVS1+PS2_Supporting+PM2_Supporting). CONCLUSION: The fetus was diagnosed with CSS type 2, and the heterozygous c.553C>T (p.Gln185Ter) variant of the ARID1A gene probably underlay its brain malformations.

Locus coeruleus microstructural integrity is associated with vigilance vulnerability to sleep deprivation.

Insufficient sleep compromises cognitive performance, diminishes vigilance, and disrupts daily functioning in hundreds of millions of people worldwide. Despite extensive research revealing significant variability in vigilance vulnerability to sleep deprivation, the underlying mechanisms of these individual differences remain elusive. Locus coeruleus (LC) plays a crucial role in the regulation of sleep-wake cycles and has emerged as a potential marker for vigilance vulnerability to sleep deprivation. In this study, we investigate whether LC microstructural integrity, assessed by fractional anisotropy (FA) through diffusion tensor imaging (DTI) at baseline before sleep deprivation, can predict impaired psychomotor vigilance test (PVT) performance during sleep deprivation in a cohort of 60 healthy individuals subjected to a rigorously controlled in-laboratory sleep study. The findings indicate that individuals with high LC FA experience less vigilance impairment from sleep deprivation compared with those with low LC FA. LC FA accounts for 10.8% of the variance in sleep-deprived PVT lapses. Importantly, the relationship between LC FA and impaired PVT performance during sleep deprivation is anatomically specific, suggesting that LC microstructural integrity may serve as a biomarker for vigilance vulnerability to sleep loss.

Multi-omics based artificial intelligence for cancer research.

With significant advancements of next generation sequencing technologies, large amounts of multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have been accumulated, offering an unprecedented opportunity to explore the heterogeneity and complexity of cancer across various molecular levels and scales. One of the promising aspects of multi-omics lies in its capacity to offer a holistic view of the biological networks and pathways underpinning cancer, facilitating a deeper understanding of its development, progression, and response to treatment. However, the exponential growth of data generated by multi-omics studies present significant analytical challenges. Processing, analyzing, integrating, and interpreting these multi-omics datasets to extract meaningful insights is an ambitious task that stands at the forefront of current cancer research. The application of artificial intelligence (AI) has emerged as a powerful solution to these challenges, demonstrating exceptional capabilities in deciphering complex patterns and extracting valuable information from large-scale, intricate omics datasets. This review delves into the synergy of AI and multi-omics, highlighting its revolutionary impact on oncology. We dissect how this confluence is reshaping the landscape of cancer research and clinical practice, particularly in the realms of early detection, diagnosis, prognosis, treatment and pathology. Additionally, we elaborate the latest AI methods for multi-omics integration to provide a comprehensive insight of the complex biological mechanisms and inherent heterogeneity of cancer. Finally, we discuss the current challenges of data harmonization, algorithm interpretability, and ethical considerations. Addressing these challenges necessitates a multidisciplinary collaboration, paving the promising way for more precise, personalized, and effective treatments for cancer patients.

Chemometrics and sensomics-assisted identification of key odorants responsible for retort odor in shelf-stored green tea infusion: A case study of Biluochun.

The deterioration of aroma quality in tea beverages during the shelf life is a significant issue. In this study, sensomics techniques were employed to identify the characteristic factor contributing to aroma degradation in green tea infusion. Samples A (no/faint retort odor) and B (high intensity retort odor) were selected based on their retort-like odor intensity after heat treatment simulating shelf-life conditions. The key odorants were identified through a combination of chemometrics analysis, comparative aromatic extract dilution analysis (cAEDA), detection frequency analysis (DFA), and odor-specific magnitude estimation (OSME). Subsequently, eight odorants, including linalool (892.451 µg/L), (E)-ß-damascenone (5.105 µg/L), phenylacetaldehyde (27.720 µg/L), nonanal (2201.439 µg/L), α-terpineol (7.166 µg/L), geraniol (0.499 µg/L), theaspirane (0.044 µg/L), and 2-hydroxy-5-methylacetophenone (2.973 µg/L), were identified as the key substances contributing to the retort-like odor in sample B. Aroma recombination and omission test further demonstrated that elevated concentrations of nonanal, geraniol, phenylacetaldehyde, and theaspirane might be the primary reasons for the retort odor observed in samples.

Insight into the photodegradation of methylisothiazolinone and benzoisothiazolinone in aquatic environments.

Methylisothiazolinone (MIT) and Benzisothiazolinone (BIT) are two widely used non-oxidizing biocides of isothiazolinones. Their production and usage volume have sharply increased since the pandemic of COVID-19, inevitably leading to more release into water environment. However, their photochemical behaviors in water environment are still unclear. Therefore, this study investigated photodegradation properties of MIT and BIT in natural water under simulated sunlight. The results demonstrated that direct photolysis was mainly responsible for their photodegradation which occurred through their excited singlet states rather than triplet states. The quantum yields of MIT and BIT photodegradation were 11 - 13.6 × 10-4 and 2.43 - 5.79 × 10-4, respectively. pH had almost no effect on the photodegradation of MIT, while the photodegradation of BIT was significantly promoted under alkaline condition due to abundance of BIT in its deprotonated form (BIT-N-). Cl-, NO3- and dissolved organic matter (DOM) in natural water inhibited the photodegradation of both MIT and BIT, with the light screening effect of DOM being the most significantly inhibitory factor. The addition of other isothiazolinones, which possibly coexisted with MIT and BIT in actual condition, slightly inhibited the photodegradation of MIT and BIT. The estimated half-life under natural sunlight at a 30°N latitude was estimated to be approximately 1.1 days. The photodegradation pathways of MIT and BIT are similar, primarily initiated from the ring-opening at the N-S bond, with Frontier electron densities (FED) calculations suggesting the likelihood of oxidation and ·OH addition reactions at the O, N, and S sites. While the photodegradation products exhibited significantly reduced acute toxicity compared to their parent compounds, they nonetheless posed substantial chronic toxicity. These insights are vital for assessing the ecological impacts of MIT and BIT in aquatic environments.

Impact of heat treatment on the flavor stability of Longjing green tea beverages: Metabolomic insights and sensory correlations.

The flavor stability of tea beverages during storage has long been a concern. The study aimed to explore the flavor stability of Longjing green tea beverage using accelerated heat treatment trials, addressing the shortage of lengthy storage trials. Sensory evaluations revealed changes in bitterness, umami, overall harmonization, astringency, and ripeness as treatment duration increased. Accompanied by a decrease in L-values, ΔE and an increase in a and b-values. Seventeen non-volatile metabolites and three volatile metabolites were identified differential among samples by metabolomics, with subsequent correlation analysis indicating associations between sensory attributes and specific metabolites. Umami was linked to epigallocatechin 3,5-digallate and alpha-D-glucopyranose, astringency was correlated with ellagic acid and 1-ethyl-1H-pyrrole. Ripeness showed associations with ellagic acid, 6,7-dihydroxycoumarin, heptanal, and benzaldehyde, and overall harmonization was linked to 6,7-dihydroxycoumarin, ß-myrcene, α-terpineol, and heptanal. A series of verification tests confirmed the feasibility of accelerated heat treatment trials to replace traditional storage trials. These results offer valuable insights into unraveling the complex relationship between sensory and chemical profiles of green tea beverages.

The common pathogenesis of nodular goiter in both sexes: An exploration into gene expression and signaling pathways.

The past few years have witnessed an increasing incidence of nodular goiter (NG), with a well-documented higher prevalence in females than males. This gender disparity has led research to focus primarily on female subjects, potentially overlooking common pathogenic mechanisms in both sexes. In this study, we investigated the shared pathogenesis of NG in males and females. Utilizing a rat model and RNA sequencing, we identified differentially expressed genes associated with the disease. We further validated these findings in normal human thyroid cells and human papillary thyroid cancer cells. A randomized experiment was conducted with equal numbers of male and female rats divided into control and NG model groups. The NG model was established using propylthiouracil and various assessments such as thyroid ultrasonography, thyroid index, thyroid function, and thyroid histology were performed. Transcriptome analysis revealed numerous upregulated and downregulated genes in both male and female model groups. Key genes like KDR, FLT1, PDGFB, and CAV1, and pathways including PI3K-Akt, MAPK, Ras, fluid shear stress and atherosclerosis, calcium signaling, and Rap1 signaling pathways were linked with the disease. Western blot and immunofluorescence analysis confirmed these findings, which were further supported by cell-based experiments. In conclusion, our findings suggest that abnormal expression of specific genes and pathways leading to irregular cell growth, blood vessel formation, and inflammation may be common factors in the pathogenesis of NG in both males and females.

A Programmable Microfluidic Paper-Based Analytical Device for Simultaneous Colorimetric and Photothermal Visual Sensing of Multiple Enzyme Activities.

New strategies for the simultaneous and portable detection of multiple enzyme activities are highly desirable for clinical diagnosis and home care. However, the methods developed thus far generally suffer from high costs, cumbersome procedures, and heavy reliance on large-scale instruments. To satisfy the actual requirements of rapid, accurate, and on-site detection of multiple enzyme activities, we report herein a smartphone-assisted programmable microfluidic paper-based analytical device (µPAD) that utilizes colorimetric and photothermal signals for simultaneous, accurate, and visual quantitative detection of alkaline phosphatase (ALP) and butyrylcholinesterase (BChE). Specifically, the operation of this µPAD sensing platform is based on two sequential steps. Cobalt-doped mesoporous cerium oxide (Co-m-CeO2) with remarkable peroxidase-like activities under neutral conditions first catalytically decomposes H2O2 for effectively converting colorless 3,3',5,5'-tetramethylbenzidine (TMB) into blue oxidized TMB (oxTMB). The subsequent addition of ALP or BChE to their respective substrates produces a reducing substance that can somewhat inhibit the oxTMB transformation for compromised colorimetric and photothermal signals of oxTMB. Notably, these two-step bioenzyme-nanozyme cascade reactions strongly support the straightforward and excellent processability of this platform, which exhibit lower detection limits for ALP and BChE with a detection limit for BChE an order of magnitude lower than those of the other reported paper-based detection methods. The practicability and efficiency of this platform are further demonstrated through the analysis of clinical serum samples. This innovative platform exhibits great potential as a facile yet robust approach for simultaneous, accurate, and on-site visual detection of multiple enzyme activities in authentic samples.

Elucidating the photodegradation mechanism of octylisothiazolinone and dichlorooctylisothiazolinone in surface water: An in-depth comprehensive analysis.

Octylisothiazolinone (OIT) and Dichlorooctylisothiazolinone (DCOIT), widely used antibacterial agents in coatings, have seen a sharp increase in use in response to the Coronavirus disease 2019 (Covid-19) pandemic, ultimately leading to their increase in the aquatic environment. However, their photodegradation process in surface water is still unclear. The purpose of this study is to investigate the photodegradation kinetics and mechanisms of OIT and DCOIT in natural water environments. Under simulated solar irradiation, they undergo direct photolysis in both natural freshwater and seawater mainly via their excited singlet states, while no self-sensitization photolysis was observed. The direct photolysis rate constants of OIT and DCOIT were 1.19 ± 0.07 and 0.57 ± 0.03 h-1, respectively. In addition, dissolved organic matter (DOM), NO3- and Cl- in natural waters did not contribute significantly to the photodegradation, and the light screening effect of DOM was identified as the main inhibiting factor. The photodegradation half-life of OIT was estimated to be 0.66 to 1.69 days, while the half-life of DCOIT was as high as 20.9 days during winter in surface water at 30°N latitude. Ring opening of the N-S bond and covalent bond breaking between CN are the main pathways for the photodegradation of OIT and DCOIT, which is verified by density-functional theory calculations. Ecological Structure Activity Relationships (ECOSAR) results indicate that OIT and DCOIT have "Very Toxic" biological toxicity, and the acute toxicity of their products is significantly reduced. It is noteworthy that the toxicity of the products of DCOIT is generally higher than that of OIT, and the chronic toxicity of most of the products is still above the "Toxic" level. Therefore, an in-depth understanding of the photodegradation mechanisms of OIT and DCOIT in aqueous environments is crucial for accurately assessing their ecological risks in natural water environments.

Characterization and Comparison of Contrast Imaging Properties of Naturally Isolated and Heterologously Expressed Gas Vesicles.

Nanoscale ultrasound contrast agents have attracted considerable interest in the medical imaging field for their ability to penetrate tumor vasculature and enable targeted imaging of cancer cells by attaching to tumor-specific ligands. Despite their potential, traditional chemically synthesized contrast agents face challenges related to complex synthesis, poor biocompatibility, and inconsistent imaging due to non-uniform particle sizes. To address these limitations, bio-synthesized nanoscale ultrasound contrast agents have been proposed as a viable alternative, offering advantages such as enhanced biocompatibility, consistent particle size for reliable imaging, and the potential for precise functionalization to improve tumor targeting. In this study, we successfully isolated cylindrical gas vesicles (GVs) from Serratia. 39006 and subsequently introduced the GVs-encoding gene cluster into Escherichia coli using genetic engineering techniques. We then characterized the contrast imaging properties of two kinds of purified GVs, using in vitro and in vivo methods. Our results demonstrated that naturally isolated GVs could produce stable ultrasound contrast signals in murine livers and tumors using clinical diagnostic ultrasound equipment. Additionally, heterologously expressed GVs from gene-engineered bacteria also exhibited good ultrasound contrast performance. Thus, our study presents favorable support for the application of genetic engineering techniques in the modification of gas vesicles for future biomedical practice.