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PURPOSE: Female adolescents are more prone to depression compared with male adolescents, and depression is often associated with poor attentional control and problematic internet use (PIU). Attentional control includes both focusing and shifting functions. Focusing refers to the ability to direct attention to a task despite distractions. Shifting refers to the ability to flexibly switch attention between different tasks. This study examines the mediating role of attentional focusing and shifting between depression and PIU. METHODS: Female high school and college students (n = 476) in Taiwan participated in a survey administered at three time points over a seven-month period. The single- and two- mediator models examined their joint mediation effects. RESULTS: The results showed that the direct effect of depression on PIU was significant. The indirect effects also showed that attentional focusing was a negative partial mediator in the relationship between depression and PIU, while attentional shifting was not one. CONCLUSION: Informed by attentional theory and digital emotion regulation, addressing depression to mitigate its negative impact on attentional control and negative emotions is crucial. The potential impact, whether harmful or beneficial, of adolescents carefully selecting their online activities to reduce the risk of PIU remains contentious. Given the focus on Taiwanese adolescent girls during the COVID-19 pandemic, further research is needed to extend the applicability of the theory to other periods without being affected by COVID-19.
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BACKGROUND: Vitamin D supplementation may prevent acute respiratory infections (ARIs). This study aimed to identify the optimal methods of vitamin D supplementation. METHODS: PubMed, Embase, Cochrane Central Register of Controlled Trials, Web of Science, and the ClinicalTrials.gov registry were searched from database inception through July 13, 2023. Randomized-controlled trials (RCTs) were included. Data were pooled using random-effects model. The primary outcome was the proportion of participants with one or more ARIs. RESULTS: The analysis included 43 RCTs with 49320 participants. Forty RCTs were considered to be at low risk for bias. The main pairwise meta-analysis indicated there were no significant preventive effects of vitamin D supplementation against ARIs (risk ratio [RR]: 0.99, 95% confidence interval [CI]: 0.97 to 1.01, I2 = 49.6%). The subgroup dose-response meta-analysis indicated that the optimal vitamin D supplementation doses ranged between 400-1200 IU/day for both summer-sparing and winter-dominant subgroups. The subgroup pairwise meta-analysis also revealed significant preventive effects of vitamin D supplementation in subgroups of daily dosing (RR: 0.92, 95% CI: 0.85 to 0.99, I2 = 55.7%, number needed to treat [NNT]: 36), trials duration < 4 months (RR: 0.81, 95% CI: 0.67 to 0.97, I2 = 48.8%, NNT: 16), summer-sparing seasons (RR: 0.85, 95% CI: 0.74 to 0.98, I2 = 55.8%, NNT: 26), and winter-dominant seasons (RR: 0.79, 95% CI: 0.71 to 0.89, I2 = 9.7%, NNT: 10). CONCLUSION: Vitamin D supplementation may slightly prevent ARIs when taken daily at doses between 400 and 1200 IU/d during spring, autumn, or winter, which should be further examined in future clinical trials.
Subject(s)
Dietary Supplements , Randomized Controlled Trials as Topic , Respiratory Tract Infections , Vitamin D , Humans , Vitamin D/administration & dosage , Vitamin D/therapeutic use , Respiratory Tract Infections/prevention & control , Dose-Response Relationship, Drug , Seasons , Acute Disease , Vitamins/administration & dosageABSTRACT
Electrocatalytic nitrogen reduction reaction (NRR) is considered to be a viable contender for the production of NH3. However, due to the sluggish adsorption and activation of the electrocatalyst toward inert N2 molecules, there is an urgent need for developing effective catalysts to facilitate the reaction. Inspired by natural nitrogenase, in which Mo atoms are the active centers, Mo-based electrocatalysts have received considerable attention, but further exploration is still necessary. Interface-engineered electrocatalysts can effectively optimize the absorption and activation of the catalytic active center for N2 and thus improve the electrocatalytic activity of NRR. However, the lack of studies for controllably constructing an optimal ratio of two phases at the interface hinders the development of NRR electrocatalysts. Herein, a series of Mo2C/MoO2 interface-engineered electrocatalysts with various Mo2C/MoO2 ratios were constructed by controlling the Y dosages. The controlled experimental results verified that the catalytic activity of NRR, the dosage of Y, and the ratio of Mo2C/MoO2 were strongly correlated. Density functional theory calculations show that the C-Mo-O coordination at the Mo2C/MoO2 interface can optimize the reaction path and reduce the energy barrier of the reaction intermediates, thereby enhancing the reaction kinetics of NRR.
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BACKGROUND AND AIMS: Risk stratification for patients with a higher risk of hepatocellular carcinoma (HCC) is crucial. We aimed to investigate the role of the Fibrosis-4 (FIB-4) index in predicting chronic hepatitis C (CHC)-related HCC. METHODS: A retrospective cohort study consecutively included treatment-naive CHC patients receiving longitudinal follow-up at the National Taiwan University Hospital from 1986 to 2014. The clinical data were collected and traced for HCC development. Multivariable Cox proportional hazard regression analysis was used to investigate the predictors for HCC. RESULTS: A total of 1285 patients in the ERADICATE-C cohort were included. The median age was 54, 56% were females, and 933 had HCV viremia. There were 33%, 38%, and 29% of patients having FIB-4 index <1.45, 1.45-3.25, and ≥3.25, respectively. After a median of 9-year follow-up, 186 patients developed HCC. Multivariable analysis revealed that older age, AFP≥20 ng/mL, cirrhosis, and a higher FIB-4 index were independent predictors for HCC. Compared with patients with FIB-4 index <1.45, those with FIB-4 1.45-3.25 had a 5.51-fold risk (95% confidence interval [CI]: 2.65-11.46), and those with FIB-4 ≥ 3.25 had 7.45-fold risk (95% CI: 3.46-16.05) of HCC. In CHC patients without viremia, FIB-4 index 1.45-3.25 and FIB-4 ≥ 3.25 increased 6.78-fold and 16.77-fold risk of HCC, respectively, compared with those with FIB-4 < 1.45. CONCLUSION: The baseline FIB-4 index can stratify the risks of HCC in untreated CHC patients, even those without viremia. The FIB-4 index should thus be included in the management of CHC.
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BACKGROUND: Genotyping isolates of a specific pathogen may demonstrate unique patterns of antimicrobial resistance, virulence or outcomes. However, evidence for genotype-outcome association in Candida glabrata is scarce. We aimed to characterize the mycological and clinical relevance of genotypes on C. glabrata bloodstream infections (BSIs). METHODS: Non-duplicated C. glabrata blood isolates from hospitalized adults were genotyped by MLST, and further clustered by the unweighted pair group method with arithmetic averages (UPGMA). A clonal complex (CC) was defined by UPGMA similarities of >90%. Antifungal susceptibility testing was performed by a colorimetric microdilution method and interpreted following CLSI criteria. RESULTS: Of 48 blood isolates evaluated, 13 STs were identified. CC7 was the leading CC (nâ=â14; 29.2%), including 13 ST7. The overall fluconazole and echinocandin resistance rates were 6.6% and 0%, respectively. No specific resistance patterns were associated with CC7 or other CCs. Charlson comorbidity index (adjusted OR, 1.49; 95% CI, 1.05-3.11) was the only predictor for CC7. By multivariable Cox regression analyses, CC7 was independently associated with 28â day mortality [adjusted HR (aHR), 3.28; 95% CI, 1.31-8.23], even after considering potential interaction with neutropenia (aHR, 3.41; 95% CI, 1.23-9.42; P for interaction, 0.24) or limited to 34 patients with monomicrobial BSIs (aHR, 2.85; 95% CI, 1.15-7.08). Also, the Kaplan-Meier estimate showed greater mortality with CC7 (Pâ=â0.003). Fluconazole resistance or echinocandin therapy had no significant impact on mortality. CONCLUSIONS: Our data suggested comorbid patients were at risk of developing CC7 BSIs. Further, CC7 was independently associated with worse outcomes.
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Antifungal Agents , Candida glabrata , Candidemia , Drug Resistance, Fungal , Genotype , Microbial Sensitivity Tests , Multilocus Sequence Typing , Humans , Candida glabrata/genetics , Candida glabrata/drug effects , Candida glabrata/isolation & purification , Male , Female , Middle Aged , Candidemia/microbiology , Candidemia/drug therapy , Candidemia/mortality , Aged , Antifungal Agents/pharmacology , Antifungal Agents/therapeutic use , Prognosis , Drug Resistance, Fungal/genetics , Adult , Aged, 80 and over , Fluconazole/pharmacology , Fluconazole/therapeutic useABSTRACT
As a promising model, genome-based plant breeding has greatly promoted the improvement of agronomic traits. Traditional methods typically adopt linear regression models with clear assumptions, neither obtaining the linkage between phenotype and genotype nor providing good ideas for modification. Nonlinear models are well characterized in capturing complex nonadditive effects, filling this gap under traditional methods. Taking populus as the research object, this paper constructs a deep learning method, DCNGP, which can effectively predict the traits including 65 phenotypes. The method was trained on three datasets, and compared with other four classic models-Bayesian ridge regression (BRR), Elastic Net, support vector regression, and dualCNN. The results show that DCNGP has five typical advantages in performance: strong prediction ability on multiple experimental datasets; the incorporation of batch normalization layers and Early-Stopping technology enhancing the generalization capabilities and prediction stability on test data; learning potent features from the data and thus circumventing the tedious steps of manual production; the introduction of a Gaussian Noise layer enhancing predictive capabilities in the case of inherent uncertainties or perturbations; fewer hyperparameters aiding to reduce tuning time across datasets and improve auto-search efficiency. In this way, DCNGP shows powerful predictive ability from genotype to phenotype, which provide an important theoretical reference for building more robust populus breeding programs.
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Genome, Plant , Neural Networks, Computer , Phenotype , Plant Breeding , Populus , Populus/genetics , Genome, Plant/genetics , Plant Breeding/methods , Deep Learning , Genotype , Bayes TheoremABSTRACT
Alcohol-associated liver disease (ALD) is a leading factor of liver-related death worldwide. ALD has various manifestations that include steatosis, hepatitis, and cirrhosis and is currently without approved pharmacotherapies. The Src homology phosphatase 2 (Shp2) is a drug target in some cancers due to its positive regulation of Ras-mitogen-activated protein kinase signaling and cell proliferation. Shp2 pharmacological inhibition yields beneficial outcomes in animal disease models, but its impact on ALD remains unexplored. This study aims to investigate the effects of Shp2 inhibition and its validity using a preclinical mouse model of ALD. We report that the administration of SHP099, a potent and selective allosteric inhibitor of Shp2, partially ameliorated ethanol-induced hepatic injury, inflammation, and steatosis in mice. Additionally, Shp2 inhibition was associated with reduced ethanol-evoked activation of extracellular signal-regulated kinase (ERK), oxidative, and endoplasmic reticulum (ER) stress in the liver. Besides the liver, excessive alcohol consumption induces multi-organ injury and dysfunction, including the intestine. Notably, Shp2 inhibition diminished ethanol-induced intestinal inflammation and permeability, abrogated the reduction in tight junction protein expression, and the activation of ERK and stress signaling in the ileum. Collectively, Shp2 pharmacological inhibition mitigates the deleterious effects of ethanol in the liver and intestine in a mouse model of ALD. Given the multifactorial aspects underlying ALD pathogenesis, additional studies are needed to decipher the utility of Shp2 inhibition alone or as a component in a multitherapeutic regimen to combat this deadly malady.
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Disease Models, Animal , Ethanol , Liver Diseases, Alcoholic , Mice, Inbred C57BL , Protein Tyrosine Phosphatase, Non-Receptor Type 11 , Animals , Liver Diseases, Alcoholic/pathology , Liver Diseases, Alcoholic/prevention & control , Liver Diseases, Alcoholic/enzymology , Liver Diseases, Alcoholic/drug therapy , Mice , Male , Protein Tyrosine Phosphatase, Non-Receptor Type 11/antagonists & inhibitors , Protein Tyrosine Phosphatase, Non-Receptor Type 11/metabolism , Ethanol/toxicity , Liver/drug effects , Liver/pathology , Liver/enzymology , Liver/metabolism , Endoplasmic Reticulum Stress/drug effects , Oxidative Stress/drug effectsABSTRACT
Accurately predicting molecular properties is a challenging but essential task in drug discovery. Recently, many mono-modal deep learning methods have been successfully applied to molecular property prediction. However, mono-modal learning is inherently limited as it relies solely on a single modality of molecular representation, which restricts a comprehensive understanding of drug molecules. To overcome the limitations, we propose a multimodal fused deep learning (MMFDL) model to leverage information from different molecular representations. Specifically, we construct a triple-modal learning model by employing Transformer-Encoder, Bidirectional Gated Recurrent Unit (BiGRU), and graph convolutional network (GCN) to process three modalities of information from chemical language and molecular graph: SMILES-encoded vectors, ECFP fingerprints, and molecular graphs, respectively. We evaluate the proposed triple-modal model using five fusion approaches on six molecule datasets, including Delaney, Llinas2020, Lipophilicity, SAMPL, BACE, and pKa from DataWarrior. The results show that the MMFDL model achieves the highest Pearson coefficients, and stable distribution of Pearson coefficients in the random splitting test, outperforming mono-modal models in accuracy and reliability. Furthermore, we validate the generalization ability of our model in the prediction of binding constants for protein-ligand complex molecules, and assess the resilience capability against noise. Through analysis of feature distributions in chemical space and the assigned contribution of each modal model, we demonstrate that the MMFDL model shows the ability to acquire complementary information by using proper models and suitable fusion approaches. By leveraging diverse sources of bioinformatics information, multimodal deep learning models hold the potential for successful drug discovery.
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BACKGROUND: Carbapenem-resistant Klebsiella pneumoniae (CRKP) poses a substantial healthcare challenge. This study assessed the in vitro efficacy of selected antibiotic combinations against CRKP infections. METHODS: Our research involved the evaluation of 40 clinical isolates of CRKP, with half expressing Klebsiella pneumoniae carbapenemase (KPC) and half producing Metallo-ß-lactamase (MBL), two key enzymes contributing to carbapenem resistance. We determined the minimum inhibitory concentrations (MICs) of four antibiotics: eravacycline, tigecycline, polymyxin-B, and ceftazidime/avibactam. Synergistic interactions between these antibiotic combinations were examined using checkerboard and time-kill analyses. RESULTS: We noted significant differences in the MICs of ceftazidime/avibactam between KPC and MBL isolates. Checkerboard analysis revealed appreciable synergy between combinations of tigecycline (35%) or eravacycline (40%) with polymyxin-B. The synergy rates for the combination of tigecycline or eravacycline with polymyxin-B were similar among the KPC and MBL isolates. These combinations maintained a synergy rate of 70.6% even against polymyxin-B resistant isolates. In contrast, combinations of tigecycline (5%) or eravacycline (10%) with ceftazidime/avibactam showed significantly lower synergy than combinations with polymyxin-B (P < 0.001 and P = 0.002, respectively). Among the MBL CRKP isolates, only one exhibited synergy with eravacycline or tigecycline and ceftazidime/avibactam combinations, and no synergistic activity was identified in the time-kill analysis for these combinations. The combination of eravacycline and polymyxin-B demonstrated the most promising synergy in the time-kill analysis. CONCLUSION: This study provides substantial evidence of a significant synergy when combining tigecycline or eravacycline with polymyxin-B against CRKP strains, including those producing MBL. These results highlight potential therapeutic strategies against CRKP infections.
Subject(s)
Azabicyclo Compounds , Bacterial Proteins , Carbapenem-Resistant Enterobacteriaceae , Klebsiella Infections , Tetracyclines , Humans , Ceftazidime/therapeutic use , Tigecycline/pharmacology , Carbapenems/pharmacology , Carbapenems/therapeutic use , Klebsiella pneumoniae , Klebsiella Infections/drug therapy , Anti-Bacterial Agents/pharmacology , Anti-Bacterial Agents/therapeutic use , beta-Lactamases/pharmacology , Polymyxins/pharmacology , Polymyxins/therapeutic use , Microbial Sensitivity TestsSubject(s)
Adenosine Monophosphate , Adenosine Monophosphate/analogs & derivatives , Adenosine/analogs & derivatives , Alanine , Alanine/analogs & derivatives , Antiviral Agents , COVID-19 Drug Treatment , Drug Therapy, Combination , Immunocompromised Host , SARS-CoV-2 , Humans , Adenosine Monophosphate/therapeutic use , Adenosine Monophosphate/administration & dosage , Alanine/therapeutic use , Alanine/administration & dosage , Antiviral Agents/therapeutic use , Antiviral Agents/administration & dosage , SARS-CoV-2/drug effects , Male , Middle Aged , Female , Aged , Treatment Outcome , COVID-19 , Antibodies, Monoclonal, Humanized/therapeutic use , Antibodies, Monoclonal, Humanized/administration & dosageABSTRACT
BACKGROUND: Patients with hematological malignancies (HM) were at a high risk of developing severe disease from coronavirus disease 2019 (COVID-19). We aimed to assess the clinical outcome of COVID-19 in hospitalized patients with HM. METHODS: Adult patients with HM who were hospitalized with a laboratory-confirmed COVID-19 between May, 2021 and November, 2022 were retrospectively identified. Primary outcome was respiratory failure requiring mechanical ventilation or mortality within 60 days after hospitalization. We also analyzed associated factors for de-isolation (defined as defervescence with a consecutive serial cycle threshold value > 30) within 28 days. RESULTS: Of 152 eligible patients, 22 (14.5%) developed respiratory failure or mortality in 60 days. Factors associated with developing respiratory failure that required mechanical ventilation or mortality included receipt of allogeneic hematopoietic stem-cell transplantation (allo-HSCT) (adjusted hazards ratio [aHR], 5.10; 95% confidence interval [CI], 1.64-15.85), type 2 diabetes mellitus (aHR, 2.47; 95% CI, 1.04-5.90), lymphopenia at admission (aHR, 6.85; 95% CI, 2.45-19.15), and receiving <2 doses of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines (aHR, 3.00; 95% CI, 1.19-7.60). Ninety-nine (65.1%) patients were de-isolated in 28 days, against which two hazardous factors were identified: receipt of B-cell depletion therapies within one year prior to COVID-19 (aHR, 0.55, 95% CI, 0.35-0.87) and lymphopenia upon admission (aHR, 0.65; 95% CI, 0.43-1.00). CONCLUSION: We found a high rate of respiratory failure and mortality among patients with HM who contracted the SARS-CoV-2. Factors associated with developing respiratory failure or mortality in 60 days included receipt of allo-HSCT, type 2 diabetes mellitus and lymphopenia upon admission. Having received ≥2 doses of vaccination conferred protection against clinical progression.
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COVID-19 , Hematologic Neoplasms , Hematopoietic Stem Cell Transplantation , SARS-CoV-2 , Humans , COVID-19/complications , COVID-19/mortality , COVID-19/epidemiology , Hematologic Neoplasms/complications , Male , Middle Aged , Female , Risk Factors , Retrospective Studies , Aged , Adult , Hematopoietic Stem Cell Transplantation/adverse effects , Severity of Illness Index , Respiratory Insufficiency/epidemiology , Respiration, Artificial , Hospitalization/statistics & numerical data , Lymphopenia , Diabetes Mellitus, Type 2/complicationsABSTRACT
BACKGROUND: The RECOVERY trial demonstrated that the use of dexamethasone is associated with a 36% lower 28-day mortality in hospitalized patients with COVID-19 on invasive mechanical ventilation. Nevertheless, the optimal timing to start dexamethasone remains uncertain. METHODS: We conducted a quasi-experimental study at National Taiwan University Hospital (Taipei, Taiwan) using propensity score matching to simulate a randomized controlled trial to receive or not to receive early dexamethasone (6 mg/day) during the first 7 days following the onset of symptoms. Treatment was standard protocol-based, except for the timing to start dexamethasone, which was left to physicians' decision. The primary outcome is 28-day mortality. Secondary outcomes include secondary infection within 60 days and fulfilling the criteria of de-isolation within 20 days. RESULTS: A total of 377 patients with COVID-19 were enrolled. Early dexamethasone did not decrease 28-day mortality in all patients (adjusted odds ratio [aOR], 1.03; 95% confidence interval [CI], 0.97-1.10) or in patients who required O2 for severe/critical disease at admission (aOR, 1.05; 95%CI, 0.94-1.18); but is associated with a 24% increase in superinfection in all patients (aOR, 1.24; 95% CI, 1.12-1.37) and a 23% increase in superinfection in patients of O2 for several/critical disease at admission (aOR, 1.23; 95% CI, 1.02-1.47). Moreover, early dexamethasone is associated with a 42% increase in likelihood of delayed clearance of SARS-CoV-2 virus (adjusted hazard ratio, 1.42; 95% CI, 1.01-1.98). CONCLUSION: An early start of dexamethasone (within 7 days after the onset of symptoms) could be harmful to hospitalized patients with COVID-19.
Subject(s)
COVID-19 Drug Treatment , COVID-19 , Dexamethasone , Propensity Score , SARS-CoV-2 , Humans , Dexamethasone/therapeutic use , Dexamethasone/administration & dosage , Male , Female , COVID-19/mortality , Middle Aged , Taiwan/epidemiology , Aged , SARS-CoV-2/drug effects , Treatment Outcome , Respiration, Artificial/statistics & numerical data , Aged, 80 and over , Hospitalization/statistics & numerical data , AdultABSTRACT
Nuclear receptor binding SET domain (NSD) proteins are a class of histone lysine methyltransferases and implicated in multiple cancer types with aberrant expression and involvement of cancer related signaling pathways. In this study, a series of small-molecule compounds including compound 2 and 3 are identified against the SET domain of NSDs through structure-based virtual screening. Our lead compound 3 exhibits potent inhibitory activities in vitro towards the NSD2-SET and NSD3-SET with an IC50 of 0.81 µM and 0.84 µM, respectively, and efficiently inhibits histone H3 lysine 36 dimethylation and decreases the expression of NSDs-targeted genes in non-small cell lung cancer cells at 100 nM. Compound 3 suppresses cell proliferation and reduces the clonogenicity in H460 and H1299 non-small cell lung cancer cells, and induces s-phase cell cycle arrest and apoptosis. These data establish our compounds as a valuable tool-kit for the study of the biological roles of NSDs in cancer.
Subject(s)
Carcinoma, Non-Small-Cell Lung , Lung Neoplasms , Humans , Histone-Lysine N-Methyltransferase/metabolism , Lysine , Repressor Proteins/metabolismABSTRACT
BACKGROUND: Cancer is a major disease that threatens human life and health. Raman spectroscopy can provide an effective detection method. OBJECTIVE: The study aimed to introduce the application of Raman spectroscopy to tumor detection. We have introduced the current mainstream Raman spectroscopy technology and related application research. METHODS: This article has first introduced the grim situation of malignant tumors in the world. The advantages of tumor diagnosis based on Raman spectroscopy have also been analyzed. Secondly, various Raman spectroscopy techniques applied in the medical field are introduced. Several studies on the application of Raman spectroscopy to tumors in different parts of the human body are discussed. Then the advantages of combining deep learning with Raman spectroscopy in the diagnosis of tumors are discussed. Finally, the related problems of tumor diagnosis methods based on Raman spectroscopy are pointed out. This may provide useful clues for future work. CONCLUSION: Raman spectroscopy can be an effective method for diagnosing tumors. Moreover, Raman spectroscopy diagnosis combined with deep learning can provide more convenient and accurate detection results.
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Traditional molecular de novo generation methods, such as evolutionary algorithms, generate new molecules mainly by linking existing atomic building blocks. The challenging issues in these methods include difficulty in synthesis, failure to achieve desired properties, and structural optimization requirements. Advances in deep learning offer new ideas for rational and robust de novo drug design. Deep learning, a branch of machine learning, is more efficient than traditional methods for processing problems, such as speech, image, and translation. This study provides a comprehensive overview of the current state of research in de novo drug design based on deep learning and identifies key areas for further development. Deep learning-based de novo drug design is pivotal in four key dimensions. Molecular databases form the basis for model training, while effective molecular representations impact model performance. Common DL models (GANs, RNNs, VAEs, CNNs, DMs) generate drug molecules with desired properties. The evaluation metrics guide research directions by determining the quality and applicability of generated molecules. This abstract highlights the foundational aspects of DL-based de novo drug design, offering a concise overview of its multifaceted contributions. Consequently, deep learning in de novo molecule generation has attracted more attention from academics and industry. As a result, many deep learning-based de novo molecule generation types have been actively proposed.
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Branched flows occur ubiquitously in various wave systems, when the propagating waves encounter weak correlated scattering potentials. Here we report the experimental realization of electrical tuning of the branched flow of light using a nematic liquid crystal (NLC) system. We create the physical realization of the weakly correlated disordered potentials of light via the inhomogeneous orientations of the NLC. We demonstrate that the branched flow of light can be switched on and off as well as tuned continuously through the electro-optical properties of NLC film. We further show that the branched flow can be manipulated by the polarization of the incident light due to the optical anisotropy of the NLC film. The nature of the branched flow of light is revealed via the unconventional intensity statistics and the rapid fidelity decay along the light propagation. Our study unveils an excellent platform for the tuning of the branched flow of light which creates a testbed for fundamental physics and offers a new way for steering light.
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Background: Protein arginine methyltransferase (PRMT) family members have important roles in cancer processes. However, its functions in the regulation of cancer immunotherapy of hepatocellular carcinoma (HCC) are incompletely understood. This study aimed to investigate the roles of PRMT1 in HCC. Methods: Single-cell RNA sequencing (scRNA-seq) and clinicopathological data were obtained and used to explore the diagnostic and prognostic value, cellular functions and roles in immune microenvironment regulation of PRMT1 in HCC. The functions of PRMT1 were explored using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO), as well as gene set enrichment analysis (GSEA). TIMER and CIBERSORT were used to analyze the relationships between PRMT1 expression and immune cell infiltration. The STRING database was used to construct a protein-protein interaction (PPI) network. Results: PRMT1 was aberrantly expressed in HCC, which high expression was associated with tumor progression, worse overall survival (OS) and disease-free survival (DFS) of patients with HCC. PRMT1 was also associated with immune cell infiltration. Moreover, it was specifically expressed in immune cells, including exhausted CD8 T cells, B cells, and mono/macro cells in patients with immunotherapy. The expression of immune checkpoints was significantly increased in the high-PRMT1 expression groups of HCC patients. Regarding biological mechanisms, cell viability, migration and invasion, and the expression of genes related to fatty acid metabolism were suppressed in PRMT1 knockdown HCC cells. Moreover, genes co-expressed with PRMT1 were involved in the fatty acid metabolic process and enriched in fatty and drug-induced liver disease. Conclusion: Taken together, these results indicate that PRMT1 might exert its oncogenic effects via immune microenvironment regulation and fatty acid metabolism in HCC. Our finding will provide a foundation for further studies and indicate a potential clinical therapeutic target for liver cancer.
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OBJECTIVE: Universal admission screening and follow-up symptom-based testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may play critical roles in controlling nosocomial transmission. We describe the performance of test strategies for inpatients and their companions during various disease incidences in Taiwan. DESIGN: Retrospective population-based cohort study. SETTING: The study was conducted across 476 hospitals in Taiwan. METHODS: The data for both testing strategies by reverse transcription-polymerase chain reaction for SARS-CoV-2 in newly admitted patients and their companions during May 2021-June 2022 were extracted and analyzed. RESULTS: The positivity rate of universal admission screening was 0.76% (14,640 of 1,928,676) for patients and 0.37% (5,372 of 1,438,944) for companions. The weekly community incidences of period 1 (May 2021-June 2021), period 2 (July 2021-March 2022), and period 3 (April 2022-June 2022) were 6.57, 0.27, and 1,261, respectively, per 100,000 population. The positivity rates of universal admission screening for patients and companions (4.39% and 2.18%) in period 3 were higher than those in periods 1 (0.29% and 0.04%) and 2 (0.03% and 0.003%) (all P < .01). Among the 22,201 confirmed cases, 9.86% were identified by symptom-based testing. The costs and potential savings of universal admission screening for patients and companions achieved a breakeven point when the test strategy was implemented in a period with weekly community incidences of 27 and 358 per 100,000 population, respectively. CONCLUSIONS: Universal admission screening and follow-up symptom-based testing is important for reducing nosocomial transmission. Implementing universal admission screening at an appropriate time would balance the benefits with costs and potential unintended harms.