Induction of Petite Colonies in Candida glabrate via Rose Bengal-Mediated Photodynamic Therapy.

Facing a 40% mortality rate in candidemia patients, drug-resistant Candida and their petite mutants remain a major treatment challenge. Antimicrobial photodynamic therapy (aPDT) targets multiple fungal structures, unlike antibiotics/antifungals, potentially thwarting resistance. Traditional methods for inducing petite colonies rely on ethidium bromide or fluconazole, which can influence drug susceptibility and stress responses. This study investigated the application of green light (peak 520 nm) and rose bengal (RB) photosensitizer to combat a drug-resistant Candida glabrata isolate. The findings revealed that aPDT treatment significantly inhibited cell growth (≥99.9% reduction) and effectively induced petite colony formation, as evidenced by reduced size and loss of mitochondrial redox indicator staining. This study provides initial evidence that aPDT can induce petite colonies in a multidrug-resistant C. glabrata strain in vitro, offering a potentially transformative approach for combating resistant fungal infections.

Unveiling the Incidence and Graft Survival Rate in Kidney Transplant Recipients With De Novo Thrombotic Microangiopathy: A Systematic Review and Meta-Analysis.

De novo thrombotic microangiopathy (TMA) is a rare and challenging condition in kidney transplant recipients, with limited research on its incidence and impact on graft survival. This study conducted a systematic review and meta-analysis of 28 cohorts/single-arm studies and 46 case series/reports from database inception to June 2022. In meta-analysis, among 14,410 kidney allograft recipients, de novo TMA occurred in 3.20% [95% confidence interval (CI): 1.93-4.77], with systemic and renal-limited TMA rates of 1.38% (95% CI: 06.5-2.39) and 2.80% (95% CI: 1.27-4.91), respectively. The overall graft loss rate of de novo TMA was 33.79% (95% CI: 26.14-41.88) in meta-analysis. This study provides valuable insights into the incidence and graft outcomes of de novo TMA in kidney transplant recipients.

Alternative therapy for acute appendicitis with appendicolith using traditional Chinese medicine: A case report.

INTRODUCTION: Treating older patients with acute appendicitis with multiple underlying diseases is a challenging situation in the clinical setting. Throughout the history of East Asian medicine, many texts have noted the use of herbal decoctions to successfully treat symptoms associated with appendicitis, thereby providing an alternative management strategy for treating frail patients with acute appendicitis. CASE PRESENTATION: This report presents the case of a 93-year-old male diagnosed with acute appendicitis with appendicolith who was unsuitable for surgical intervention due to poor lung condition and prolonged prothrombin time caused by Apixaban. By receiving complementary therapy consisting of traditional Chinese medicine (TCM) and antibiotics, he recovered successfully. DISCUSSION: In the early stage of acute appendicitis, infection control and gastrointestinal mobility are the most important factors to consider. Scientific research has demonstrated that many herbs used in TCM formulas possess anti-inflammatory, anti-oxidative and immune attenuation effects. For many patients who are unsuitable for surgical intervention due to unfavorable conditions, such as the prolonged prothrombin time presented in this case, complementary treatment with TCM offers adjuvant therapeutical effects associated with infection control, and improvement of the gastrointestinal environment and function of elderly patients.

Assessing sugar-sweetened beverage consumption in early pregnancy using a substance abuse framework.

Sugar-sweetened beverages (SSB) are previously reported to jeopardize maternal fetal health, most well-known for gestational diabetes, childhood obesity, and cognitive impairment. Although warnings and diet recommendations urge pregnant women to consume less SSB, there is no noticeable difference in their behavior. How and why reproductive women change their SSB consumption patterns were not investigated previously. Our study aims to investigate beverage consumption patterns and how these patterns change in pregnancy in the context of substance use disorder (SUD). We invited all pregnant women visiting the clinic to answer a structured 20-min questionnaire every trimester during the regular antennal visit. At the end of the study, 337 pregnant women aged over 20 participated. A total of 301 responses entered for final analysis, with a response rate of 89.3%. Our finding showed those with high DSM-5-TR scores reduced SSB intake after becoming pregnant, while those with mild or low DSM-5-TR scores increased SSB intake after becoming pregnant. The top 3 factors related to their SSB consumption were "use despite of known health hazard (n = 133)", "increased desire to drink (n = 88)", and "excessive time spent on seeking SSB (n = 85)". The least reported factors were in the domains of social impairment (ranging from n = 3 to n = 26), pharmacologic effects (i.e., tolerance (n = 24) and withdrawal (n = 70). When participants reduced SSB consumption after becoming pregnant, their choice of beverages largely shifted to sugarless beverage but not much plain water. The result provided new insights in deciphering pregnant women's psychomotor factors for SSB intake, which served as useful references for making clinical or even public health recommendations.

Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks.

The electrochemical ammonia oxidation to dinitrogen as a means for energy and environmental applications is a key technology toward the realization of a sustainable nitrogen cycle. The state-of-the-art metal catalysts including Pt and its bimetallics with Ir show promising activity, albeit suffering from high overpotentials for appreciable current densities and the soaring price of precious metals. Herein, the immense design space of ternary Pt alloy nanostructures is explored by graph neural networks trained on ab initio data for concurrently predicting site reactivity, surface stability, and catalyst synthesizability descriptors. Among a few Ir-free candidates that emerge from the active learning workflow, Pt3Ru-M (M: Fe, Co, or Ni) alloys were successfully synthesized and experimentally verified to be more active toward ammonia oxidation than Pt, Pt3Ir, and Pt3Ru. More importantly, feature attribution analyses using the machine-learned representation of site motifs provide fundamental insights into chemical bonding at metal surfaces and shed light on design strategies for high-performance catalytic systems beyond the d-band center metric of binding sites.

Prescription Pattern in Angiotensin Receptor Neprilysin Inhibitor is Associated with Heart Failure Events.

Background: Angiotensin receptor neprilysin inhibition (ARNI) is superior to enalapril in reducing the risk of cardiovascular death and heart failure hospitalization (HFH). However, whether prescription pattern is associated with heart failure outcome is unknown. Methods: This is a retrospective study of 153 patients who received ARNI in a tertiary medical center in Taiwan. We analyzed the impact of dose up-titration and prescription timing including during initial admission, within 3 months after initial HFH discharge, and at outpatient clinics without prior HFH. The primary endpoint was the composite of cardiovascular death and HFH. Results: After a mean follow-up period of 287 ± 197 days, the primary endpoint occurred in 43 (28.1%) subjects. Patients without and with a primary endpoint significantly differed in terms of history of valvular heart disease (VHD, p = 0.006), ventricular tachyarrhythmia (VT, p = 0.043), percutaneous coronary intervention (p = 0.007), coronary artery bypass grafting (p = 0.002), chronic kidney disease (p = 0.002), age (p = 0.002), diastolic blood pressure (p = 0.025), and prescription timing (p = 0.002). Kaplan-Meier analysis showed ARNI up-titration and prescription timing had a significant association with primary endpoint-free survival (Breslow test; p = 0.032, and log-rank test; p = 0.001, respectively). Cox regression analysis showed that independent predictors for the primary endpoint were ARNI up-titration [hazard ratio (HR): 0.41, p = 0.024], non-hospital ARNI versus hospital ARNI (HR: 0.41, p = 0.009), VHD (HR: 2.71, p = 0.013), VT (HR: 3.09, p = 0.02), and age (HR: 1.03, p = 0.033). Conclusions: The prescription pattern of ARNI could be associated with heart failure events.

Upregulation of PD-L1 by SARS-CoV-2 promotes immune evasion.

Patients with severe COVID-19 often suffer from lymphopenia, which is linked to T-cell sequestration, cytokine storm, and mortality. However, it remains largely unknown how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces lymphopenia. Here, we studied the transcriptomic profile and epigenomic alterations involved in cytokine production by SARS-CoV-2-infected cells. We adopted a reverse time-order gene coexpression network approach to analyze time-series RNA-sequencing data, revealing epigenetic modifications at the late stage of viral egress. Furthermore, we identified SARS-CoV-2-activated nuclear factor-κB (NF-κB) and interferon regulatory factor 1 (IRF1) pathways contributing to viral infection and COVID-19 severity through epigenetic analysis of H3K4me3 chromatin immunoprecipitation sequencing. Cross-referencing our transcriptomic and epigenomic data sets revealed that coupling NF-κB and IRF1 pathways mediate programmed death ligand-1 (PD-L1) immunosuppressive programs. Interestingly, we observed higher PD-L1 expression in Omicron-infected cells than SARS-CoV-2 infected cells. Blocking PD-L1 at an early stage of virally-infected AAV-hACE2 mice significantly recovered lymphocyte counts and lowered inflammatory cytokine levels. Our findings indicate that targeting the SARS-CoV-2-mediated NF-κB and IRF1-PD-L1 axis may represent an alternative strategy to reduce COVID-19 severity.

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

Deglycosylation of SLAMF7 in breast cancers enhances phagocytosis.

N-linked glycosylation of proteins is one of the post-translational modifications (PTMs) that shield tumor antigens from immune attack. Signaling lymphocytic activation molecule family 7 (SLAMF7) suppresses cancer cell phagocytosis and is an ideal target under clinical development. PTM of SLAMF7, however, remains less understood. In this study, we investigated the role of N-glycans on SLAMF7 in breast cancer progression. We identified seven N-linked glycosylation motifs on SLAMF7, which are majorly occupied by complex structures. Evolutionally conserved N98 residue is enriched with high mannose and sialylated glycans. Hyperglycosylated SLAMF7 was associated with STT3A expression in breast cancer cells. Inhibition of STT3A by a small molecule inhibitor, N-linked glycosylation inhibitor-1 (NGI-1), reduced glycosylation of SLAMF7, resulting in enhancing antibody affinity and phagocytosis. To provide an on-target effect, we developed an antibody-drug conjugate (ADC) by coupling the anti-SLAMF7 antibody with NGI-1. Deglycosylation of SLAMF7 increases antibody recognition and promotes macrophage engulfment of breast cancer cells. Our work suggests deglycosylation by ADC is a potential strategy to enhance the response of immunotherapeutic agents.

OpenFL: the open federated learning library.

Objective.Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) and deep learning (DL) projects without sharing sensitive data, such as patient records, financial data, or classified secrets.Approach.Open federated learning (OpenFL) framework is an open-source python-based tool for training ML/DL algorithms using the data-private collaborative learning paradigm of FL, irrespective of the use case. OpenFL works with training pipelines built with both TensorFlow and PyTorch, and can be easily extended to other ML and DL frameworks.Main results.In this manuscript, we present OpenFL and summarize its motivation and development characteristics, with the intention of facilitating its application to existing ML/DL model training in a production environment. We further provide recommendations to secure a federation using trusted execution environments to ensure explicit model security and integrity, as well as maintain data confidentiality. Finally, we describe the first real-world healthcare federations that use the OpenFL library, and highlight how it can be applied to other non-healthcare use cases.Significance.The OpenFL library is designed for real world scalability, trusted execution, and also prioritizes easy migration of centralized ML models into a federated training pipeline. Although OpenFL's initial use case was in healthcare, it is applicable beyond this domain and is now reaching wider adoption both in research and production settings. The tool is open-sourced atgithub.com/intel/openfl.

Prinsepiae Nux Extract Activates NRF2 Activity and Protects UVB-Induced Damage in Keratinocyte.

Ultraviolet B (UVB) is one of the most important environmental factors that cause extrinsic aging through increasing intracellular reactive oxygen species (ROS) production in the skin. Due to its protective roles against oxidative stress, nuclear factor erythroid-2-related factor (NRF2) has been traditionally considered as a target for skin aging prevention. Here, we identified the extract of Prinsepiae Nux, a top-grade drug listed in Shen Nong Ben Cao Jing, as a potent NRF2 activator by high-throughput screening. A bioassay-guided fractionation experiment revealed that NRF2-activating components were concentrated in the 90% methanol (MP) fraction. MP fraction significantly increased the expression of NRF2 and HO-1 protein and upregulated HO-1 and NQO1 mRNA expression in HaCaT cells. Moreover, MP fraction pre-treatment dramatically reversed UVB-induced depletion of NRF2 and HO-1, accumulation of intracellular ROS, NF-κB activation, and the upregulation of pro-inflammatory genes. Finally, the qualitative analysis using UPLC-tandem mass spectroscopy revealed the most abundant ion peak in MP fraction was identified as α-linolenic acid, which was further proved to activate NRF2 signaling. Altogether, the molecular evidence suggested that MP fraction has the potential to be an excellent source for the discovery of natural medicine to treat/prevent UVB-induced skin damage.

Prognostic serum biomarkers in cancer patients with COVID-19: A systematic review.

PURPOSE: Cancer patients with COVID-19 likely express biomarker changes in circulation. However, the biomarkers used in SARS-CoV-2 infected cancer patients for COVID-19 severity and prognosis are largely unclear. Therefore, this systematic review aims to determine what biomarkers were measured in cancer patients with COVID-19 and their prognostic utility. METHODS: A systematic literature review in PubMed, Embase, and Scopus was performed on June 16th, 2021. The search keywords coronavirus, neoplasm, biomarkers, and disease progression were used to filter out 17 eligible studies, which were then carefully evaluated. RESULTS: A total of 4,168 patients, 16 types of cancer, and 60 biomarkers were included. Seven up-regulated markers, including CRP, d-dimer, ferritin, IL-2R, IL-6, LDH, and PCT, were identified in eligible studies. Albumin and hemoglobin were significantly down-regulated in cancer patients with COVID-19. Moreover, we observed that the SARS-CoV-2 infected cancer patients with lower CRP, ferritin, and LDH levels successfully survived from COVID-19 treatments. CONCLUSION: Several important clinical biomarkers, such as CRP, ferritin, and LDH, may serve as the prognostic markers to predict the outcomes following COVID-19 treatment and monitor the deterioration of COVID-19 in cancer patients.

Comparison of effectiveness and safety between ticagrelor and clopidogrel in patients with acute coronary syndrome and on dialysis in Taiwan.

AIMS: This study aimed to evaluate and compare the effectiveness and safety between clopidogrel and ticagrelor in acute coronary syndrome (ACS) with renal dysfunction. METHODS: We conducted a retrospective cohort study on patients on chronic dialysis and whose admission diagnosis between 1 July 2013 and 31 December 2016 included ACS. The primary effectiveness endpoint was a major adverse cardiovascular event (MACE), and the primary safety endpoint was a major bleeding event. The application of propensity scores through the inverse probability of treatment weighting (IPTW) was applied to the study groups. Cox regression was used to estimate hazard ratios (aHRs) of study endpoints. In addition, the competing risk was adjusted using the Fine and Gray competing risk model. RESULTS: There were 1915 patients in the clopidogrel group and 270 patients in the ticagrelor group. At 12 months, the ticagrelor group had higher risks for MACE (aHR with IPTW: 1.29; 95% CI 1.16-1.44); death (aHR with IPTW: 1.65; 95% CI 1.47-1.86) and cardiac death (subdistribution HR [SHR] with IPTW: 1.64; 95% CI 1.41-1.91), compared with those in the clopidogrel group. For major bleeding event, the risk was significantly higher with ticagrelor than with clopidogrel (SHR with IPTW: 1.49; 95% CI 1.34-1.65). In terms of the risk for any bleeding event, there was no significant difference between the two groups (SHR with IPTW: 1.05; 95% CI 0.95-1.17). CONCLUSIONS: Compared with clopidogrel, ticagrelor was associated with higher MACE, death, cardiac death and major bleeding risk within 12 months in patients with ACS and on dialysis.

Targeting conserved N-glycosylation blocks SARS-CoV-2 variant infection in vitro.

BACKGROUND: Despite clinical success with anti-spike vaccines, the effectiveness of neutralizing antibodies and vaccines has been compromised by rapidly spreading SARS-CoV-2 variants. Viruses can hijack the glycosylation machinery of host cells to shield themselves from the host's immune response and attenuate antibody efficiency. However, it remains unclear if targeting glycosylation on viral spike protein can impair infectivity of SARS-CoV-2 and its variants. METHODS: We adopted flow cytometry, ELISA, and BioLayer interferometry approaches to assess binding of glycosylated or deglycosylated spike with ACE2. Viral entry was determined by luciferase, immunoblotting, and immunofluorescence assays. Genome-wide association study (GWAS) revealed a significant relationship between STT3A and COVID-19 severity. NF-κB/STT3A-regulated N-glycosylation was investigated by gene knockdown, chromatin immunoprecipitation, and promoter assay. We developed an antibody-drug conjugate (ADC) that couples non-neutralization anti-spike antibody with NGI-1 (4G10-ADC) to specifically target SARS-CoV-2-infected cells. FINDINGS: The receptor binding domain and three distinct SARS-CoV-2 surface N-glycosylation sites among 57,311 spike proteins retrieved from the NCBI-Virus-database are highly evolutionarily conserved (99.67%) and are involved in ACE2 interaction. STT3A is a key glycosyltransferase catalyzing spike glycosylation and is positively correlated with COVID-19 severity. We found that inhibiting STT3A using N-linked glycosylation inhibitor-1 (NGI-1) impaired SARS-CoV-2 infectivity and that of its variants [Alpha (B.1.1.7) and Beta (B.1.351)]. Most importantly, 4G10-ADC enters SARS-CoV-2-infected cells and NGI-1 is subsequently released to deglycosylate spike protein, thereby reinforcing the neutralizing abilities of antibodies, vaccines, or convalescent sera and reducing SARS-CoV-2 variant infectivity. INTERPRETATION: Our results indicate that targeting evolutionarily-conserved STT3A-mediated glycosylation via an ADC can exert profound impacts on SARS-CoV-2 variant infectivity. Thus, we have identified a novel deglycosylation method suitable for eradicating SARS-CoV-2 variant infection in vitro. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Hyperglycosylated spike of SARS-CoV-2 gamma variant induces breast cancer metastasis.

SARS-CoV-2 exploits the host cellular machinery for virus replication leading to the acute syndrome of coronavirus disease 2019 (COVID-19). Growing evidence suggests SARS-CoV-2 also exacerbates many chronic diseases, including cancers. As mutations on the spike protein (S) emerged as dominant variants that reduce vaccine efficacy, little is known about the relation between SARS-CoV-2 virus variants and cancers. Compared to the SARS-CoV-2 wild-type, the Gamma variant contains two additional NXT/S glycosylation motifs on the S protein. The hyperglycosylated S of Gamma variant is more stable, resulting in more significant epithelial-mesenchymal transition (EMT) potential. SARS-CoV-2 infection promoted NF-κB signaling activation and p65 nuclear translocation, inducing Snail expression. Pharmacologic inhibition of NF-κB activity by nature food compound, I3C suppressed viral replication and Gamma variant-mediated breast cancer metastasis, indicating that NF-κB inhibition can reduce chronic disease in COVID-19 patients. Our study revealed that the Gamma variant of SARS-CoV-2 activates NF-κB and, in turn, triggers the pro-survival function for cancer progression.

Interpretable Machine Learning of Chemical Bonding at Solid Surfaces.

Understanding the nature of chemical bonding and its variation in strength across physically tunable factors is important for the development of novel catalytic materials. One way to speed up this process is to employ machine learning (ML) algorithms with online data repositories curated from high-throughput experiments or quantum-chemical simulations. Despite the reasonable predictive performance of ML models for predicting reactivity properties of solid surfaces, the ever-growing complexity of modern algorithms, e.g., deep learning, makes them black boxes with little to no explanation. In this Perspective, we discuss recent advances of interpretable ML for opening up these black boxes from the standpoints of feature engineering, algorithm development, and post hoc analysis. We underline the pivotal role of interpretability as the foundation of next-generation ML algorithms and emerging AI platforms for driving discoveries across scientific disciplines.

Infusing theory into deep learning for interpretable reactivity prediction.

Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. Herein, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance while being inherently interpretable. Incorporation of scientific knowledge of physical interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties.

Increased sugar-sweetened beverage use tendency in pregnancy positively associates with peripartum Edinburgh postpartum depression scores.

The association among sugar sweetened beverages (SSB) consumption, addiction and depression in adults, children and adolescents is widely reported. Dieting patterns during pregnancy is complicated by maternal fetal concerns. Specifically, restrained use of SSB might be potentially a source of perinatal distress. The current study modified diagnostic criteria for Substance Use Disorder (SUD) in Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), into SSB-specific questions to assess SSB use tendency. Edinburgh Postpartum Depression Scores (EPDS) is used to assess maternal distress during pregnancy. One hundred and ninety-six consecutive pregnant women receiving antenatal care at Kaohsiung Medical University Hospital were invited to participate in this study. In the first trimester, 46.6% of women had none or 1 DSM-5 symptom, 27.0% had 2-3 symptoms, while 26.4% had ≥ 4 symptoms. The mean numbers of DSM-5 symptoms in each trimester were found to be 2.5 ± 2.25, 2.6 ± 2.45, 2.4 ± 2.43 for the first, second and third trimester, respectively, p = 0.750. While EPDS score showed no difference among DSM-5 symptoms 0-1, 2-3 and ≥ 4 groups in the first trimester (8.1 ± 4.59, 8.4 ± 5.00, 8.8 ± 4.82, p = 0.343), women with ≥ 4 DSM-symptoms was found significantly higher EPDS scores than those with < 4 DSM-symptoms in the second (7.2 ± 4.81, 7.7 ± 4.98, 8.8 ± 4.33, p = 0.030) and third trimester (6.8 ± 5.00, 7.2 ± 4.63, 8.7 ± 5.24, p = 0.019). The relationship remained significant after adjusting for covariates including actual SSB amount consumed (adjusted ß = 0.25 with 95% confident interval (CI) 0.04-0.45 and 0.21 with 95% CI 0.04-0.38 for the second and third trimesters, respectively). Overall, the study is the first to characterize the positive relationship between SSB use tendency and antenatal distress in pregnancy, independent of actual SSB amount consumed. The observational nature of the study design precludes inferences of its underlying socio-psychomotor mechanisms, although restrained SSB use in pregnancy is suspected to contribute. The novel employment of modified SSB-specific DSM-5 scores and EPDS in this setting is feasible and further validation is promising. With better understanding and awareness, pregnant women with increased SSB use tendency should be properly counseled with special attention to their mental state.