Atomization by Acoustic Levitation Facilitates Contactless Microdroplet Reactions.

Microdroplet chemistry is now well-known to be able to remarkably accelerate otherwise slow reactions and trigger otherwise impossible reactions. The uniqueness of the microdroplet is attributable to either the air-water interface or solid-liquid interface, depending on the medium that the microdroplet is in contact with. To date, the importance of the solid-liquid interface might have been confirmed, but the contribution from the air-water interface seems to be elusive due to the lack of method for generating contactless microdroplets. In this study, we used a droplet atomization method with acoustic levitation. Upon manipulation of the acoustic field, the levitated parent droplet can be further atomized into progeny microdroplets. With this method, only the air-water interface was present, and a large variety of reactions were successfully tested. We anticipate that this study can be an advance toward the understanding of the air-water interfacial processes of microdroplet chemistry.

Spontaneous Generation of Alkoxide Radical from Alcohols on Microdroplets Surface.

Water microdroplets have been demonstrated to exhibit extraordinary chemical behaviors, including the abilities to accelerate chemical reactions by several orders of magnitude and to trigger reactions that cannot occur in bulk water. One of the most striking examples is the spontaneous generation of hydroxyl radical from hydroxide ions. Alcohols and alkoxide ions, being structurally similar to water and hydroxide ions, might exhibit similar behavior on microdroplets. Here, we report the spontaneous generation of alkoxide radicals from alcohols (RCH2OH) in aqueous microdroplets through quantum chemical calculations, quantum mechanical/molecular mechanical (QM/MM) molecular dynamics (MD) simulations, ab initio MD simulations, and mass spectrometry. Our results show that an electric field (EF) on the order of 10-1 V/Šand partial solvation at the air‒water interface jointly promote the dissociation of RCH2OH into RCH2O- and H3O+ ions. QM/MM MD simulations indicate that RCH2O- can be ionized to produce RCH2O• radicals on the microdroplet surface. Furthermore, partial solvation and the EF collaboratively catalyze the isomerization of the RCH2O• radical into a more stable tautomer, R•CHOH. This study highlights the molecular mechanisms underlying the widespread generation of radicals at the microdroplet surface and provides insights into the importance of fundamental alcohol chemistry in the atmosphere.

Development and Validation of Machine Learning Algorithms for Prediction of Colorectal Polyps Based on Electronic Health Records.

BACKGROUND: Colorectal Polyps are the main source of precancerous lesions in colorectal cancer. To increase the early diagnosis of tumors and improve their screening, we aimed to develop a simple and non-invasive diagnostic prediction model for colorectal polyps based on machine learning (ML) and using accessible health examination records. METHODS: We conducted a single-center observational retrospective study in China. The derivation cohort, consisting of 5426 individuals who underwent colonoscopy screening from January 2021 to January 2024, was separated for training (cohort 1) and validation (cohort 2). The variables considered in this study included demographic data, vital signs, and laboratory results recorded by health examination records. With features selected by univariate analysis and Lasso regression analysis, nine machine learning methods were utilized to develop a colorectal polyp diagnostic model. Several evaluation indexes, including the area under the receiver-operating-characteristic curve (AUC), were used to compare the predictive performance. The SHapley additive explanation method (SHAP) was used to rank the feature importance and explain the final model. RESULTS: 14 independent predictors were identified as the most valuable features to establish the models. The adaptive boosting machine (AdaBoost) model exhibited the best performance among the 9 ML models in cohort 1, with accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and AUC (95% CI) of 0.632 (0.618-0.646), 0.635 (0.550-0.721), 0.674 (0.591-0.758), 0.593 (0.576-0.611), 0.673 (0.654-0.691), 0.608 (0.560-0.655) and 0.687 (0.626-0.749), respectively. The final model gave an AUC of 0.675 in cohort 2. Additionally, the precision recall (PR) curve for the AdaBoost model reached the highest AUPR of 0.648, positioning it nearest to the upper right corner. SHAP analysis provided visualized explanations, reaffirming the critical factors associated with the risk of colorectal polyps in the asymptomatic population. CONCLUSIONS: This study integrated the clinical and laboratory indicators with machine learning techniques to establish the predictive model for colorectal polyps, providing non-invasive, cost-effective screening strategies for asymptomatic individuals and guiding decisions for further examination and treatment.

Controlled bioorthogonal activation of Bromodomain-containing protein 4 degrader by co-delivery of PROTAC and Pd-catalyst for tumor-specific therapy.

The precise and safe treatment of bioorthogonal prodrug system is hindered by separate administration of prodrug and its activator, which often results in poor therapeutic effects and severe side effects. To address above issues, we herein construct a single bioorthogonal-activated co-delivery system for simultaneous PROTAC prodrug (proPROTAC) delivery and controlled, site-specific activation for tumor-specific treatment. In this co-delivery system (termed AuPLs), prodrug (proPROTAC) and water-soluble Pd-catalyst are first encapsulated by gold nanocubes (AuNCs), which are further coated with a layer of phase-change material (lauric acid/stearic acid, LA/SA). Below 39 °C, the solid state of LA/SA prevents the activation of Pd-mediated bioorthogonal reaction due to the solidification of Pd-catalyst and proPROTAC. Nevertheless, once over 42 °C, the phase change of LA/SA into liquid state, enabled by the photothermal effect of AuNCs, triggers the simultaneous release of proPROTAC and Pd-catalyst and initiates the in situ bioorthogonal reaction for proPROTAC activation. In the tumor-bearing mouse models, the systemic administration of AuPLs results in the accumulation in tumor region, where the photothermal effect activates and controls the tumor-specific bioorthogonal reaction to degrade BRD4 protein, leading to anti-tumor effects with minimized side effects. Overall, the co-delivery proPROTAC and Pd-catalyst and controlled activation by photothermal effects provide a precise way for biorthogonal-based anticancer prodrugs.

Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data.

BACKGROUND: Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening and staging, using routine clinical data. METHODS: Two medical center, observational, retrospective studies were conducted, involving 2312 lung cancer patients and 653 patients with benign nodules. Machine learning techniques, including differential analysis and feature selection, were employed to identify key factors for modeling. The study focused on variables such as nodule density, carcinoembryonic antigen (CEA), age, and lifestyle habits. The Logistic Regression model was utilized for early diagnoses, and the XGBoost model was utilized for staging based on selected features. RESULTS: For early diagnoses, the Logistic Regression model achieved an area under the curve (AUC) of 0.716 (95% confidence interval [CI] 0.607-0.826), with 0.703 sensitivity and 0.654 specificity. The XGBoost model excelled in distinguishing late-stage from early-stage lung cancer, exhibiting an AUC of 0.913 (95% CI 0.862-0.963), with 0.909 sensitivity and 0.814 specificity. These findings highlight the model's potential for enhancing diagnostic accuracy and staging in lung cancer. CONCLUSION: This study introduces a novel machine learning-based risk model for early lung cancer screening and staging, leveraging routine clinical information and laboratory data. The model shows promise in enhancing accuracy, mitigating overdiagnosis, and improving patient outcomes.

Iron(III)-Catalyzed Amine-Release Triple Condensation of Enaminones to C3-Alkenylated Dihydroquinolinones.

C3-functionalized dihydroquinolinones represent a class of important biologically active compounds. Although methods for synthesizing C2/4-functionalized dihydroquinolinones have been extensively reported, research on the synthesis of C3-functionalized dihydroquinolinone is extremely rare. Herein, we report for the first time a method for C3-alkenylated dihydroquinolinones via iron(III)-catalyzed amine-release triple condensation of enaminones. These reactions exhibit broad substrate scope and offer operationally simple, low-cost catalyzed procedures in a single step. Subsequent intramolecular and intermolecular additions to the alkene moiety provide diverse C3-functionalized dihydroquinolinone derivatives.

Immunological profiling for short-term predictive analysis in PD-1/PD-L1 therapy for lung cancer.

BACKGROUND: Immune checkpoint inhibitors, such as anti-programmed cell death-1 (PD-1) and PD-1 ligand-1 (PD-L1) antibodies, have achieved breakthrough results in improving long-term survival rates in lung cancer. Although high levels of PD-L1 expression and tumor mutational burden have emerged as pivotal biomarkers, not all patients derive lasting benefits, and resistance to immune checkpoint blockade remains a prevalent issue. Comprehending the immunological intricacies of lung cancer is crucial for uncovering the mechanisms that govern responses and resistance to immunomodulatory treatments. This study aimed to explore the potential of peripheral immune markers in predicting treatment efficiency among lung cancer patients undergoing PD-1/PD-L1 checkpoint inhibitors. METHODS: This study enrolled 71 lung cancer patients undergoing PD-1/PD-L1 inhibitor therapy and 20 healthy controls. Immune cell subsets (CD4 + T cells, CD8 + T cells, B cells, NK cells, and NKT cells), phenotypic analysis of T cells and B cells, and PMA/Ionomycin-stimulated lymphocyte function assay were conducted. RESULTS: Lung cancer patients exhibited significant alterations in immune cell subsets, notably an increased percentage of Treg cells. Post-treatment, there were substantial increases in absolute numbers of CD3 + T cells, CD8 + T cells, and NKT cells, along with heightened HLA-DR expression on CD3 + T and CD8 + T cells. Comparison between complete remission and non-complete remission (NCR) groups showed higher Treg cell percentages and HLA-DR + CD4 + T cells in the NCR group. CONCLUSION: The study findings suggest potential predictive roles for immune cell subsets and phenotypes, particularly Treg cells, HLA-DR + CD4 + T cells, and naïve CD4 + T cells, in evaluating short-term PD-1/PD-L1 therapy efficacy for lung cancer patients. These insights offer valuable prospects for personalized treatment strategies and underscore the importance of immune profiling in lung cancer immunotherapy.

Esophageal squamous cell carcinoma metastases to kidney and renal hilar lymph nodes through epithelial-mesenchymal transition: a case report and literature review.

BACKGROUND: Esophageal cancer (EC) metastasized to the kidney is extremely rare clinically. Here, we present a case of metachronous renal metastasis of esophageal squamous cell carcinoma (ESCC) through epithelial-mesenchymal transition (EMT). CASE PRESENTATION: A 60-year-old patient, male, complained of left waist pain for 5 days, 11 months after radical esophagectomy. Laboratory tests revealed haematuria. Both CT and PET-CT scan showed retroperitoneal lymph nodes and left renal masses. Subsequently the patient received a left nephrectomy and lymph nodes resection, and squamous cell carcinoma of kidney and renal hilar lymph nodes was diagnosed, combined with morphology, medical history and immunophenotype, it was presumed to be metastasis of ESCC through the EMT pathway. CONCLUSIONS: The renal metastasis of squamous cell carcinoma should be considered in patients with history of EC, although this is very rare. Histopathological examination combined with immunochemical detection is helpful in differential diagnosis.

Hierarchical supramolecules composed of starch-based nanocluster aggregates with light-responsive mechanical strain for remotely rapid and precise actuation.

Hierarchical supramolecular systems, characterized by nanoscale sensitivity and macroscopic tangible changes, offer promising perspectives for the design of remotely controllable, rapid, and precise actuation materials, serving as a potential substitution for non-intelligent and complex actuation switches. Herein, we reported on the disassembly of orderly and rigid starch helical covalent structures, and their subsequent reassembly into a hierarchical supramolecular gel composed of nanocluster aggregates, integrating supramolecular interactions of three different scales. The incorporation of photo-sensitive FeIIITA, a complex of trivalent iron ions and tannic acid, significantly enhances the photo-responsive strain capacity of the hierarchical supramolecular gel. The supramolecular gel exhibits its features in a rapid light-responsive rate of hardness and viscosity, enabling the actuation of objects within 22 s under light exposure when employed as a remote actuation switch. Meanwhile, this actuation mechanism of the hierarchical supramolecular gel also has a promising perspective in precise control, identifying and actuating one of the two objects in distances of 0.8 mm even smaller scales. Our work provides a reliable reference for replacing complex actuation switches with intelligent materials for remote, rapid, and accurate actuation, and offers valuable insights for actuation in harsh and vacuum outdoor environments.

A novel Burkholderia pyrrocinia strain effectively inhibits Fusarium graminearum growth and deoxynivalenol (DON) production.

BACKGROUND: Fusarium graminearum is a devastating fungal pathogen that poses a significant threat to global wheat production and quality. Control of this toxin-producing pathogen remains a major challenge. This study aimed to isolate strains with antagonistic activity against F. graminearum and at the same time to analyze the synthesis of deoxynivalenol (DON), in order to provide a new basis for the biological control of FHB. RESULTS: Total of 69 microorganisms were isolated from the soil of a wheat-corn crop rotation field, and an antagonistic bacterial strain F12 was identified as Burkholderia pyrrocinia by molecular biology and carbon source utilization. F. graminearum control by strain F12 showed excellent biological activities under laboratory conditions (95.8%) and field testing (63.09%). Meanwhile, the DON content of field-treated wheat grains was detected the results showed that F12 have significantly inhibited of DON, which was further verified by qPCR that F12 produces secondary metabolites that inhibit the expression of DON and pigment-related genes. In addition, the sterile fermentation broth of F12 not only inhibited mycelial growth and spore germination, but also prevented mycelia from producing spores. CONCLUSION: In this study B. pyrrocinia was reported to have good control of FHB and inhibition of DON synthesis. This novel B. pyrrocinia F12 is a promising biological inoculant, providing possibilities for controlling FHB, and a theoretical basis for the development of potential biocontrol agents and biofertilizers for agricultural use. © 2024 Society of Chemical Industry.

Serum and urine metabolomics based on UPLC-QTOF/MS reveal the effect and potential mechanism of "schisandra-evodia" herb pair in the treatment of Alzheimer's disease.

The "schisandra-evodia" herb pair (S-E) is a herbal preparation to treat Alzheimer's disease (AD). This study aims to investigate the therapeutic efficacy and potential mechanism of S-E in AD rats, utilizing pharmacodynamic assessments and serum- and urine-based metabolomic analyses. Pharmacodynamic assessments included Morris water maze test, hematoxylin-eosin staining and immunohistochemistry experiments. The results of the study showed that the AD model was successful; the S-E significantly enhanced long-term memory and spatial learning in AD rats. Meanwhile, S-E notably ameliorated Aß25-35-induced cognitive impairment, improved hippocampal neuron morphology, decreased Aß deposition in the hippocampus and mitigated inflammatory damage. We then analyzed serum and urine samples using UPLC-MS/MS to identify potential biomarkers and metabolic pathways. Metabolomic analysis revealed alterations in 40 serum metabolites and 38 urine metabolites following S-E treatment, predominantly affecting pathways related to taurine and hypotaurine metabolism, linoleic acid metabolism, α-linolenic acid metabolism, glycerophospholipid metabolism and arachidonic acid metabolism. This study elucidates the biochemical mechanism underlying AD and the metabolic pathway influenced by S-E, laying the groundwork for future clinical applications.

Comprehensive evaluation of immune dysregulation in secondary hemophagocytic lymphohistiocytosis.

Host immune dysfunction plays a crucial role in the onset, progression, and outcome of hemophagocytic lymphohistiocytosis (HLH). This study aimed to comprehensively evaluate the peripheral immune profiles in patients with newly diagnosed secondary hemophagocytic lymphohistiocytosis (sHLH), and explore their predictive value for patient prognosis. A total of 77 patients with sHLH were enrolled in this study, with 31 of them experiencing mortality. Flow cytometry was used to assess the percentages, absolute numbers, and phenotypes of lymphocyte subsets. Simultaneously, cytokine levels and routine laboratory indicators were also collected. In sHLH patients, lymphocyte subset absolute numbers were significantly impaired, accompanied by T cell hyperactivation, B cell hyperactivation, and increased plasmablast proliferation. Prognostic analysis revealed that lower CD8+ T cell percentages, elevated APTT, IL-6, IL-10 levels, and increased CD4+CD28null T cell proportions were associated with poor patient outcomes. The study demonstrates dysregulation in the counts and phenotypes of lymphocyte subsets in sHLH patients. Several key factors, including IL-6, IL-10, APTT, and various T cell percentages, have potential as prognostic markers and therapeutic targets in sHLH.

BadCM: Invisible Backdoor Attack Against Cross-Modal Learning.

Despite remarkable successes in unimodal learning tasks, backdoor attacks against cross-modal learning are still underexplored due to the limited generalization and inferior stealthiness when involving multiple modalities. Notably, since works in this area mainly inherit ideas from unimodal visual attacks, they struggle with dealing with diverse cross-modal attack circumstances and manipulating imperceptible trigger samples, which hinders their practicability in real-world applications. In this paper, we introduce a novel bilateral backdoor to fill in the missing pieces of the puzzle in the cross-modal backdoor and propose a generalized invisible backdoor framework against cross-modal learning (BadCM). Specifically, a cross-modal mining scheme is developed to capture the modality-invariant components as target poisoning areas, where well-designed trigger patterns injected into these regions can be efficiently recognized by the victim models. This strategy is adapted to different image-text cross-modal models, making our framework available to various attack scenarios. Furthermore, for generating poisoned samples of high stealthiness, we conceive modality-specific generators for visual and linguistic modalities that facilitate hiding explicit trigger patterns in modality-invariant regions. To the best of our knowledge, BadCM is the first invisible backdoor method deliberately designed for diverse cross-modal attacks within one unified framework. Comprehensive experimental evaluations on two typical applications, i.e., cross-modal retrieval and VQA, demonstrate the effectiveness and generalization of our method under multiple kinds of attack scenarios. Moreover, we show that BadCM can robustly evade existing backdoor defenses. Our code is available at https://github.com/xandery-geek/BadCM.

Effects of Exercise Habits and Gender on Sports e-Learning Behavior: Evidence from an Eye-Tracking Study.

Background/Objective: In the post-epidemic era, an increasing number of individuals were accustomed to learning sports and physical activity knowledge online for fitness and health demands. However, most previous studies have examined the influence of e-learning materials and resources on learners and have neglected intrinsic factors such as experience and physiological characteristics. Therefore, we conducted a study to investigate the effect of exercise habits and gender on sports e-learning behavior via eye-tracking technology. Methods: We recruited a sample of 60 undergraduate students (mean age = 19.6) from a university in Nanjing, China. They were randomly assigned into 4 groups based on 2 genders × 2 exercise habits. Their gaze behavior was collected by an eye-tracking device during the experiment. The cognitive Load Test and Learning Effect Test were conducted at the end of the individual experiment. Results: (1) Compared to the non-exercise habit group, the exercise habit group had a higher fixation count (P<0.05), a shorter average fixation duration (P<0.05), a smaller average pupil diameter (P<0.05), and a lower subjective cognitive load (P<0.05) and better learning outcome (P<0.05). (2) Male participants showed a greater tendency to process information from the video area of interest (AOIs), and had lower subjective cognitive load (P < 0.05) and better learning outcomes (P < 0.05). (3) There was no interaction effect between exercise habits and gender for any of the indicators (P > 0.05). Conclusion: Our results indicate that exercise habits effectively enhance sports e-learning outcomes and reduce cognitive load. The exercise habits group showed significant improvements in fixation counts, average fixation duration, and average pupil diameter. Furthermore, male subjects exhibited superior learning outcomes, experienced lower cognitive load, and demonstrated greater attentiveness to dynamic visual information. These conclusions are expected to improve sports e-learning success and address educational inequality.

Novel multiclass classification machine learning approach for the early-stage classification of systemic autoimmune rheumatic diseases.

OBJECTIVE: Systemic autoimmune rheumatic diseases (SARDs) encompass a diverse group of complex conditions with overlapping clinical features, making accurate diagnosis challenging. This study aims to develop a multiclass machine learning (ML) model for early-stage SARDs classification using accessible laboratory indicators. METHODS: A total of 925 SARDs patients were included, categorised into SLE, Sjögren's syndrome (SS) and inflammatory myositis (IM). Clinical characteristics and laboratory markers were collected and nine key indicators, including anti-dsDNA, anti-SS-A60, anti-Sm/nRNP, antichromatin, anti-dsDNA (indirect immunofluorescence assay), haemoglobin (Hb), platelet, neutrophil percentage and cytoplasmic patterns (AC-19, AC-20), were selected for model building. Various ML algorithms were used to construct a tripartite classification ML model. RESULTS: Patients were divided into two cohorts, cohort 1 was used to construct a tripartite classification model. Among models assessed, the random forest (RF) model demonstrated superior performance in distinguishing SLE, IM and SS (with area under curve=0.953, 0.903 and 0.836; accuracy= 0.892, 0.869 and 0.857; sensitivity= 0.890, 0.868 and 0.795; specificity= 0.910, 0.836 and 0.748; positive predictive value=0.922, 0.727 and 0.663; and negative predictive value= 0.854, 0.915 and 0.879). The RF model excelled in classifying SLE (precision=0.930, recall=0.985, F1 score=0.957). For IM and SS, RF model outcomes were (precision=0.793, 0.950; recall=0.920, 0.679; F1 score=0.852, 0.792). Cohort 2 served as an external validation set, achieving an overall accuracy of 87.3%. Individual classification performances for SLE, SS and IM were excellent, with precision, recall and F1 scores specified. SHAP analysis highlighted significant contributions from antibody profiles. CONCLUSION: This pioneering multiclass ML model, using basic laboratory indicators, enhances clinical feasibility and demonstrates promising potential for SARDs classification. The collaboration of clinical expertise and ML offers a nuanced approach to SARDs classification, with potential for enhanced patient care.

Bioequivalence and Safety Assessment of 2 Formulations of Low-Dose Metformin Hydrochloride under Fasting Conditions in Healthy Chinese Participants: A Randomized Phase 1 Clinical Trial.

The incidence of type 2 diabetes is high, and the existing metformin hydrochloride (MH) tablets of 250 mg cannot meet the demands of the Chinese drug market. This study aimed to evaluate the bioequivalence and safety of generic formulations of MH tablets (test formulation [T], 250 mg/tablet) and innovative products (reference formulation [R], 250 mg/tablet) under fasting conditions. This was an open-label, single-dose, 2-period, 2-sequence crossover, single-center, randomized phase I clinical trial. T and R were considered bioequivalent if the adjusted geometric mean ratios (GMRs) and 90% confidence intervals of the area under the curve (AUC) and maximum concentration (Cmax ) were within the range of 0.8-1.25. Thirty-five participants completed the trial. The T/R adjusted GMRs (95.7% for Cmax , 98.7% for AUC0→t , 98.8% for AUC0→∞ ) were within the acceptable bioequivalence range of 80%-125%. No serious adverse events or suspected or unexpected serious adverse reactions occurred during this trial. The study findings confirmed that generic MH is a well-tolerated and bioequivalent alternative to innovative products under fasting conditions in healthy Chinese participants. (www.chinadrugtrials.org.cn; registration no. CTR20190356).

A Novel Ultrafiltrate Extract of Propolis Exerts Anti-inflammatory Activity through Metabolic Rewiring.

Thousands of years ago, humans started to use propolis because of its medicinal properties, and modern science has successfully identified several bioactive molecules within this resinous bee product. However, a natural propolis extract which has been removed the adhesive glue and preserved propolis bioactive compounds is urgently needed to maximise the therapeutic opportunities. In this study, a novel ultrafiltrate fraction from Brazilian green propolis, termed P30K, was demonstrated with anti-inflammatory properties, both in vitro and in vivo. Total flavonoids and total phenolic acids content in P30K were 244.6 mg/g and 275.8 mg/g respectively, while the IC50 value of inhibition of cyclooxygenase-2 (COX-2) was 8.30 µg/mL. The anti-inflammatory activity of P30K was furtherly corroborated in experimental models of lipopolysaccharides (LPS)-induced acute liver and lung injury. Mechanistically, integrated GC-MS and LC-MS based serum metabolomics analysis revealed that P30K modulated citrate cycle (TCA), pyruvate, glyoxylate and dicarboxylate metabolism pathways to inhibit secretion of pro-inflammatory cytokines. Results of network pharmacology and molecular docking suggested that P30K targeted catechol-O-methyltransferases (COMT), 11ß-hydroxysteroid dehydrogenases (HSD11B1), and monoamine oxidases (MAOA and MAOB) to promote cellular metabolomic rewiring. Collectively, our work reveals P30K as an efficient therapeutic agent against inflammatory conditions and its efficacy is related to metabolic rewiring.

MicroRNA-148a-3p suppresses the glycolysis and Cell proliferation by targeting transmembrane protein 54 in liver cancer.

Liver cancer is the fourth most lethal cancer, but the treatment options for liver cancer are usually limited. Metabolic reprogramming is a hallmark of malignancy, ensuring activated cell glycolysis and increased macromolecular precursors required for the proliferation and migration of exuberant cancer cells. MicroRNAs (miRNAs) have been reported to participate in cancer metabolic shifts mainly by directly silencing the expression of specific genes. Here, we identified miR-148a-3p as a negative regulator for glycometabolism and cell proliferation in liver cancer. miR-148a-3p directly targets the 3'UTR of transmembrane protein 54 (TMEM54), leading to the significant inhibition of lactate production, glucose consumption, intracellular ATP level and extracellular acidification rate (ECAR), as well as the repression of the proliferation and colony formation ability of liver cancer cells. miR-148a-3p expression is often down-regulated in liver cancer tissues. In addition, there was a negative correlation between the expression levels of miR-148a-3p and TMEM54 in liver cancer tissues. Moreover, the low miR-148a-3p expression levels or high TMEM54 expression levels were associated with poorer prognosis in hepatocellular carcinoma (HCC) patients. Together, these findings support that the miR-148a-3p/TMEM54 regulatory pathway regulates the glycometabolism and cell proliferation in liver cancer, which is a possible target for the diagnosis and treatment of liver cancer.

Knowledge Structure and Emerging Trends of Mild Cognitive Impairment with Dyssomnias in Recent 20 Years: A Bibliometric Analysis via CiteSpace and VOSviewer.

Background: Mild cognitive impairment (MCI), an intermediate stage between normal aging and dementia, has emerged as a prominent research area in geriatric care due to its heightened propensity for progressing toward dementia. Sleep plays a pivotal role in cognitive function, with dyssomnias not only exacerbating cognitive and affective symptoms associated with neurodegenerative diseases but also contributing to disease progression. Aim: This bibliometric analysis investigates the global research on MCI with dyssomnias over the past two decades, aiming to discern key findings, research domains, and emerging trends in this field. Methods: In this study, a bibliometric analysis was conducted using the search terms "MCI" and "sleep". Data were extracted from the Web of Science Core Collection database, and visualization and collaborative analysis were performed using CiteSpace and VOSviewer. Results: This study encompassed 546 publications from 2003 to 2023. The publication volume and citation rate consistently increased over time. Neurosciences, Clinical Neurology, and Geriatrics Gerontology emerged as the top three research fields. The Journal of Alzheimer's Disease had the highest publication count, while Sleep Medicine received the most citations. USA, China, and Italy led in publication output. Collaborative clusters among authors and institutions were identified, but cooperation between clusters was limited. Active cocited reference clusters included "obstructive sleep apnea", "possible mediating pathways", and "isolated rapid eye movement sleep behaviour disorder". The top frequently mentioned keywords, besides "MCI", were "Alzheimer's disease", "dementia", "risk factor", and "Parkinson's Disease". Notable keyword clusters spanned circadian rhythm, Parkinson's disease, MCI, dementia with Lewy body, subjective cognitive impairment, Lewy body disease, Alzheimer's disease, and dietary patterns. Conclusion: The field of MCI with dyssomnias is rapidly expanding, encompassing a wide range of neurodegenerative disorders and sleep disturbances. Current research endeavors are primarily focused on elucidating the underlying pathogenesis, predicting disease progression, and developing innovative treatment strategies for individuals affected by MCI with dyssomnias.