Reliability and clinical utility of spatially constrained estimates of intrinsic functional networks from very short fMRI scans.

Resting-state functional network connectivity (rsFNC) has shown utility for identifying characteristic functional brain patterns in individuals with psychiatric and mood disorders, providing a promising avenue for biomarker development. However, several factors have precluded widespread clinical adoption of rsFNC diagnostics, namely a lack of standardized approaches for capturing comparable and reproducible imaging markers across individuals, as well as the disagreement on the amount of data required to robustly detect intrinsic connectivity networks (ICNs) and diagnostically relevant patterns of rsFNC at the individual subject level. Recently, spatially constrained independent component analysis (scICA) has been proposed as an automated method for extracting ICNs standardized to a chosen network template while still preserving individual variation. Leveraging the scICA methodology, which solves the former challenge of standardized neuroimaging markers, we investigate the latter challenge of identifying a minimally sufficient data length for clinical applications of resting-state fMRI (rsfMRI). Using a dataset containing rsfMRI scans of individuals with schizophrenia and controls (M = 310) as well as simulated rsfMRI, we evaluated the robustness of ICN and rsFNC estimates at both the subject- and group-level, as well as the performance of diagnostic classification, with respect to the length of the rsfMRI time course. We found individual estimates of ICNs and rsFNC from the full-length (5 min) reference time course were sufficiently approximated with just 3-3.5 min of data (r = 0.85, 0.88, respectively), and significant differences in group-average rsFNC could be sufficiently approximated with even less data, just 2 min (r = 0.86). These results from the shorter clinical data were largely consistent with the results from validation experiments using longer time series from both simulated (30 min) and real-world (14 min) datasets, in which estimates of subject-level FNC were reliably estimated with 3-5 min of data. Moreover, in the real-world data we found rsFNC and ICN estimates generated across the full range of data lengths (0.5-14 min) more reliably matched those generated from the first 5 min of scan time than those generated from the last 5 min, suggesting increased influence of "late scan" noise factors such as fatigue or drowsiness may limit the reliability of FNC from data collected after 10+ min of scan time, further supporting the notion of shorter scans. Lastly, a diagnostic classification model trained on just 2 min of data retained 97%-98% classification accuracy relative to that of the full-length reference model. Our results suggest that, when decomposed with scICA, rsfMRI scans of just 2-5 min show good clinical utility without significant loss of individual FNC information of longer scan lengths.

Impact of flexible noise control (FNC) image processing parameters on portable chest radiography.

There is a lack of understanding in the performance of flexible noise control (FNC) processing, which is used in digital radiography on a scanner vendor and has four parameters each involving multiple options. The aim of this study was to investigate the impact of FNC on portable chest imaging. An anthropomorphic chest phantom was imaged using a clinical chest program with 85 kV and five radiation dose levels at 40″ source-to-image distance with software-based scatter reduction method. All images were processed without and with FNC. Noise analysis was performed in two regions of interest (ROI) on subtracted noise-only images, and line profiles were generated through a lung-rib interface. In addition, noise power spectra (NPS) analysis was performed in solid water phantoms of 10 and 20 cm thicknesses, using the same acquisition program and a range of dose levels. Last, feedback on retrospectively deidentified, reprocessed, and randomized clinical images from 20 portable chest exams was gathered from two thoracic radiologists. Noise reduction performances of FNC were demonstrated, with the level depending on specific FNC parameters, dose levels, ROI placement, and phantom sizes. Higher frequency textural patterns were revealed through the NPS analysis, which varied based on FNC parameters, dose levels, and phantom sizes. Overall, the vendor default parameter FGA0.5 yielded the highest noise reduction and textural artifacts. Radiologist feedback showed consistent preference of no FNC due to the presence of textural artifacts in the FNC-processed images. An algorithm improvement to avoid introducing artifacts would be desired.

The Pyrimidine Analog FNC Potently Inhibits the Replication of Multiple Enteroviruses.

Human enteroviruses (EVs), including coxsackieviruses, the numbered enteroviruses, and echoviruses, cause a wide range of diseases, such as hand, foot, and mouth disease (HFMD), encephalitis, myocarditis, acute flaccid myelitis (AFM), pneumonia, and bronchiolitis. Therefore, broad-spectrum anti-EV drugs are urgently needed to treat EV infection. Here, we demonstrate that FNC (2'-deoxy-2'-ß-fluoro-4'-azidocytidine), a small nucleoside analog inhibitor that has been demonstrated to be a potent inhibitor of HIV and entered into a clinical phase II trial in China, potently inhibits the viral replication of a multitude of EVs, including enterovirus 71 (EV71), coxsackievirus A16 (CA16), CA6, EVD68, and coxsackievirus B3 (CVB3), at the nanomolar level. The antiviral mechanism of FNC involves mainly positive- and negative-strand RNA synthesis inhibition by targeting and competitively inhibiting the activity of EV71 viral RNA-dependent RNA polymerase (3Dpol), as demonstrated through quantitative real-time reverse transcription-PCR (RT-qPCR), in vitro 3Dpol activity, and isothermal titration calorimetry (ITC) experiments. We further demonstrated that FNC treatment every 2 days with 1 mg/kg of body weight in EV71 and CA16 infection neonatal mouse models successfully protected mice from lethal challenge with EV71 and CA16 viruses and reduced the viral load in various tissues. These findings provide important information for the clinical development of FNC as a broad-spectrum inhibitor of human EV pathogens.IMPORTANCE Human enterovirus (EV) pathogens cause various contagious diseases such as hand, foot, and mouth disease, encephalitis, myocarditis, acute flaccid myelitis, pneumonia, and bronchiolitis, which have become serious health threats. However, except for the EV71 vaccine on the market, there are no effective strategies to prevent and treat other EV pathogen infections. Therefore, broad-spectrum anti-EV drugs are urgently needed. In this study, we demonstrated that FNC, a small nucleoside analog inhibitor that has been demonstrated to be a potent inhibitor of HIV and entered into a clinical phase II trial in China, potently inhibits the viral replication of a multitude of EVs at the nanomolar level. Further investigation revealed that FNC inhibits positive- and negative-strand RNA synthesis of EVs by interacting and interfering with the activity of EV71 viral RNA-dependent RNA polymerase (3Dpol). Our findings demonstrate for the first time that FNC is an effective broad-spectrum inhibitor for human EV pathogens.

Altered static and dynamic functional network connectivity in Alzheimer's disease and subcortical ischemic vascular disease: shared and specific brain connectivity abnormalities.

Subcortical ischemic vascular disease (SIVD) is a major subtype of vascular dementia with features that overlap clinically with Alzheimer's disease (AD), confounding diagnosis. Neuroimaging is a more specific and biologically based approach for detecting brain changes and thus may help to distinguish these diseases. There is still a lack of knowledge regarding the shared and specific functional brain abnormalities, especially functional connectivity changes in relation to AD and SIVD. In this study, we investigated both static functional network connectivity (sFNC) and dynamic FNC (dFNC) between 54 intrinsic connectivity networks in 19 AD patients, 19 SIVD patients, and 38 age-matched healthy controls. The results show that both patient groups have increased sFNC between the visual and cerebellar (CB) domains but decreased sFNC between the cognitive-control and CB domains. SIVD has specifically decreased sFNC within the sensorimotor domain while AD has specifically altered sFNC between the default-mode and CB domains. In addition, SIVD has more occurrences and a longer dwell time in the weakly connected dFNC states, but with fewer occurrences and a shorter dwell time in the strongly connected dFNC states. AD has both similar and opposite changes in certain dynamic features. More importantly, the dynamic features are found to be associated with cognitive performance. Our findings highlight similar and distinct functional connectivity alterations in AD and SIVD from both static and dynamic perspectives and indicate dFNC to be a more important biomarker for dementia since its progressively altered patterns can better track cognitive impairment in AD and SIVD.

Intracellular mobility and nuclear trafficking of the stress-activated kinase JNK1 are impeded by hyperosmotic stress.

The c-Jun N-terminal kinases (JNKs) are a group of stress-activated protein kinases that regulate gene expression changes through specific phosphorylation of nuclear transcription factor substrates. To address the mechanisms underlying JNK nuclear entry, we employed a semi-intact cell system to demonstrate for the first time that JNK1 nuclear entry is dependent on the importin α2/ß1 heterodimer and independent of importins α3, α4, ß2, ß3, 7 and 13. However, quantitative image analysis of JNK1 localization following exposure of cells to either arsenite or hyperosmotic stress did not indicate its nuclear accumulation. Extending our analyses to define the dynamics of nuclear trafficking of JNK1, we combined live cell imaging analyses with fluorescence recovery after photobleaching (FRAP) protocols. Subnuclear and subcytoplasmic bleaching protocols revealed the slowed movement of JNK1 in both regions in response to hyperosmotic stress. Strikingly, while movement into the nucleus of green fluorescent protein (GFP) or transport of a GFP-T-antigen fusion protein as estimated by initial rates and time to reach half-maximal recovery (t1/2) measures remained unaltered, hyperosmotic stress slowed the nuclear entry of GFP-JNK1. In contrast, arsenite exposure which did not alter the initial rates of nuclear accumulation of GFP, GFP-T-antigen or GFP-JNK1, decreased the t1/2 for nuclear accumulation of both GFP and GFP-JNK1. Thus, our results challenge the paradigm of increased nuclear localization of JNK broadly in response to all forms of stress-activation and are consistent with enhanced interactions of stress-activated JNK1 with scaffold and substrate proteins throughout the nucleus and the cytosol under conditions of hyperosmotic stress.

Abnormal static and dynamic brain network connectivity associated with chronic tinnitus.

In order to comprehensively understand the changes of brain networks in patients with chronic tinnitus, this study combined static and dynamic analysis methods to explore the abnormalities of brain networks. Thirty-two patients with chronic tinnitus and 30 age-, sex- and education-matched healthy controls (HC) were recruited. Independent component analysis was used to identify resting-state networks (RSNs). Static and dynamic functional network connectivity (FNC) were performed. The temporal properties of brain network including mean dwell time (MDT), fraction time (FT) and numbers of transitions (NT) were calculated. Two-sample t test and Spearman's correlation were used for group compares and correlation analysis. Four RSNs showed abnormal FNC including auditory network (AUN), default mode network (DMN), attention network (AN) and sensorimotor network (SMN). For static analysis, tinnitus patients showed significantly decreased FNC in AUN-DMN, AUN-AN, DMN-AN, and DMN-SMN than HC [p < 0.05, false discovery rate (FDR) corrected]. For dynamic analysis, tinnitus patients showed significantly decreased FNC in DMN-AN in state 3 (p < 0.05, FDR corrected). MDT in state 3 was significantly decreased in tinnitus patients (t = 2.039, P = 0.046). In the tinnitus group, the score of tinnitus functional index (TFI) was negatively correlated with MDT and FT in state 4, and the duration of tinnitus was positively correlated with FT in state 1 and NT. Chronic tinnitus causes abnormal brain network connectivity. These abnormal brain networks help to clarify the mechanism of tinnitus generation and chronicity, and provide a potential basis for the treatment of tinnitus.

Aberrant Intra- and Inter-Network Connectivity in Idiopathic Sudden Sensorineural Hearing Loss with Tinnitus.

BACKGROUND: Idiopathic Sudden Sensorineural Hearing Loss (ISSNHL) is related to alterations in brain cortical and subcortical structures, and changes in brain functional activities involving multiple networks, which is often accompanied by tinnitus. There have been many in-depth research studies conducted concerning ISSNHL. Despite this, the neurophysiological mechanisms of ISSNHL with tinnitus are still under exploration. OBJECTIVE: The study aimed to investigate the neural mechanism in ISSNHL patients with tinnitus based on the alterations in intra- and inter-network Functional Connectivity (FC) of multiple networks. METHODS: Thirty ISSNHL subjects and 37 healthy subjects underwent resting-state functional Magnetic Resonance Imaging (rs-fMRI). Independent Component Analysis (ICA) was used to identify 8 Resting-state Networks (RSNs). Furthermore, the study used a two-sample t-test to calculate the intra-network FC differences, while calculating Functional Network Connectivity (FNC) to detect the inter-network FC differences. RESULTS: By using the ICA approach, tinnitus patients with ISSNHL were found to have FC changes in the following RSNs: CN, VN, DMN, ECN, SMN, and AUN. In addition, the interconnections of VN-SMN, VN-ECN, and ECN-DAN were weakened. CONCLUSION: The present study has demonstrated changes in FC within and between networks in ISSNHL with tinnitus, providing ideas for further study on the neuropathological mechanism of the disease.

A Method to Estimate Longitudinal Change Patterns in Functional Network Connectivity of the Developing Brain Relevant to Psychiatric Problems, Cognition, and Age.

Aim: To develop an approach to evaluate multiple overlapping brain functional change patterns (FCPs) in functional network connectivity (FNC) and apply to study developmental changes in brain function. Introduction: FNC, the network analog of functional connectivity (FC), is commonly used to capture the intrinsic functional relationships among brain networks. Ongoing research on longitudinal changes of intrinsic FC across whole-brain functional networks has proven useful for characterizing age-related changes, but to date, there has been little focus on capturing multivariate patterns of FNC change with brain development. Methods: In this article, we introduce a novel approach to evaluate multiple overlapping FCPs by utilizing FNC matrices. We computed FNC matrices from the large-scale Adolescent Brain Cognitive Development data using fully automated spatially constrained independent component analysis (ICA). We next evaluated changes in these patterns for a 2-year period using a second-level ICA on the FNC change maps. Results: Our proposed approach reveals several highly structured (modular) FCPs and significant results including strong brain FC between visual and sensorimotor domains that increase with age. We also find several FCPs that are associated with longitudinal changes of psychiatric problems, cognition, and age in the developing brain. Interestingly, FCP cross-covariation, reflecting coupling between maximally independent FCPs, also shows significant differences between upper and lower quartile loadings for longitudinal changes in age, psychiatric problems, and cognition scores, as well as baseline age in the developing brain. FCP patterns and results were also found to be highly reliable based on analysis of data collected in a separate scan session. Conclusion: In sum, our results show evidence of consistent multivariate patterns of functional change in emerging adolescents and the proposed approach provides a useful and general tool to evaluate covarying patterns of whole-brain functional changes in longitudinal data. Impact statement In this article, we introduce a novel approach utilizing functional network connectivity (FNC) matrices to estimate multiple overlapping brain functional change patterns (FCPs). The findings demonstrate several well-structured FCPs that exhibit significant changes for a 2-year period, particularly in the functional connectivity between the visual and sensorimotor domains. In addition, we discover several FCPs that are associated with psychopathology, cognition, and age. Finally, our proposed approach for studying age-related FCPs represents a pioneering method that provides a valuable tool for assessing interconnected patterns of whole-brain functional changes in longitudinal data and may be useful to study change over time with applicability to many other areas, including the study of longitudinal changes within diagnostic groups, treatment effects, aging effects, and more.

Parliamentary question: Insights from the Federal National Council in the UAE.

This paper highlights the paramount role of parliamentary questioning as a control mechanism exercised by the Federal National Council (FNC) in the United Arab Emirates (UAE). The surge in its utilization, attributed to heightened awareness among FNC members and ministers, has significantly enhanced the Council's control capabilities, especially in the absence of alternative parliamentary control instruments such as interpellations. The paper underscores the simplicity and adaptability of parliamentary questioning, which spans diverse topics and addresses everyday state matters, rectifies errors, monitors law implementation, and fills legislative gaps. The structure of the paper comprises two sections: the first scrutinizes procedural requirements within the UAE's Constitution and the FNC's Bylaw, while the second section explores practical examples, offering insights into the distinctive nature of the FNC's parliamentary questioning compared to regional practices.

FNC (4'-azido-2'-deoxy-2'-fluoro(arbino)cytidine) as an Effective Therapeutic Agent for NHL: ROS Generation, Cell Cycle Arrest, and Mitochondrial-Mediated Apoptosis.

Cytotoxic nucleoside analogs (NAs) hold great promise in cancer therapeutics by mimicking endogenous nucleosides and interfering with crucial cellular processes. Here, we investigate the potential of the novel cytidine analog, 4'-azido-2'-deoxy-2'-fluoro(arbino)cytidine (FNC), as a therapeutic agent for Non-Hodgkin lymphoma (NHL) using Dalton's lymphoma (DL) as a T-cell lymphoma model. FNC demonstrated dose- and time-dependent inhibition of DL cell growth and proliferation. IC-50 values of FNC were measured at 1 µM, 0.5 µM, and 0.1 µM after 24, 48, and 72 h, respectively. Further elucidation of FNC's mechanism of action uncovers its role in inducing apoptosis in DL cells. Notable DNA fragmentation and nuclear condensation point to activated apoptotic pathways. FNC-induced apoptosis was concomitant with changes in cellular membranes, characterized by membrane rupture and altered morphology. The robust anticancer effects of FNC are linked to its capacity to induce reactive oxygen species (ROS) production, prompting oxidative stress-mediated apoptosis. Additionally, FNC disrupted mitochondrial membrane potential (MMP), leading to mitochondrial dysfunction, further promoting apoptosis. Dysregulation of apoptotic genes, with upregulation of Bax and downregulation of Bcl-2 and Bcl-xl, implicates the mitochondrial-mediated apoptosis pathway. Furthermore, FNC-induced G2/M phase cell cycle arrest was mediated through modulation of the cell cycle inhibitor p21. Overall, this study highlights the potential of FNC as a promising therapeutic agent for NHL.

Synchronous reinforcement azo dyes decolorization and anaerobic granular sludge stability by Fe, N co-modified biochar: Enhancement based on extracellular electron transfer.

Anaerobic digestion (AD) treatment of azo dyes wastewater often suffers from low decolorization efficiency and poor stability of anaerobic granular sludge (AnGS). In this study, iron and nitrogen co-modified biochar (FNC) was synthesized based on the secondary calcination method, and the feasibility of this material for enhanced AD treatment of azo dye wastewater and its mechanism were investigated. FNC not only formed richer conducting functional groups, but also generated Fe2+/Fe3+ redox pairs. The decolorization efficiency of Congo red and AD properties (e.g., methane production) were enhanced by FNC. After adding FNC, the content of extracellular polymeric substances (EPS) and the ratio of proteins remained stable under the impact of Congo red, which greatly protected the internal microbial community. This was mainly contributed to the excellent electrochemical properties of FNC, which strengthened the microbial extracellular electron transfer and realized the coupled mechanism of action: On the one hand, an electron transfer bridge between decolorizing bacteria and dyes was constructed to achieve rapid decolorization of azo dyes and mitigate the impact on methanogenic bacteria; On the other hand, the stability of AnGS was enhanced based on enhanced extracellular polymeric substances secretion, microbial community and direct interspecies electron transfer (DIET) process. This study provides a new idea for enhanced AD treatment of azo dyes wastewater.

Unraveling the Neural Landscape of Mental Disorders using Double Functional Independent Primitives (dFIPs).

Mental illnesses extract a high personal and societal cost, and thus explorations of the links between mental illness and functional connectivity in the brain are critical. Investigating major mental illnesses, believed to arise from disruptions in sophisticated neural connections, allows us to comprehend how these neural network disruptions may be linked to altered cognition, emotional regulation, and social interactions. Although neuroimaging has opened new avenues to explore neural alterations linked to mental illnesses, the field still requires precise and sensitive methodologies to inspect these neural substrates of various psychological disorders. In this study, we employ a hierarchical methodology to derive double functionally independent primitives (dFIPs) from resting state functional magnetic resonance neuroimaging data (rs-fMRI). These dFIPs encapsulate canonical overlapping patterns of functional network connectivity (FNC) within the brain. Our investigation focuses on the examination of how combinations of these dFIPs relate to different mental disorder diagnoses. The central aim is to unravel the complex patterns of FNC that correspond to the diverse manifestations of mental illnesses. To achieve this objective, we used a large brain imaging dataset from multiple sites, comprising 5805 total individuals diagnosed with schizophrenia (SCZ), autism spectrum disorder (ASD), bipolar disorder (BPD), major depressive disorder (MDD), and controls. The key revelations of our study unveil distinct patterns associated with each mental disorder through the combination of dFIPs. Notably, certain individual dFIPs exhibit disorder-specific characteristics, while others demonstrate commonalities across disorders. This approach offers a novel, data-driven synthesis of intricate neuroimaging data, thereby illuminating the functional changes intertwined with various mental illnesses. Our results show distinct signatures associated with psychiatric disorders, revealing unique connectivity patterns such as heightened cerebellar connectivity in SCZ and sensory domain hyperconnectivity in ASD, both contrasted with reduced cerebellar-subcortical connectivity. Utilizing the dFIP concept, we pinpoint specific functional connections that differentiate healthy controls from individuals with mental illness, underscoring its utility in identifying neurobiological markers. In summary, our findings delineate how dFIPs serve as unique fingerprints for different mental disorders.

A whole-brain neuromark resting-state fMRI analysis of first-episode and early psychosis: Evidence of aberrant cortical-subcortical-cerebellar functional circuitry.

Psychosis (including symptoms of delusions, hallucinations, and disorganized conduct/speech) is a main feature of schizophrenia and is frequently present in other major psychiatric illnesses. Studies in individuals with first-episode (FEP) and early psychosis (EP) have the potential to interpret aberrant connectivity associated with psychosis during a period with minimal influence from medication and other confounds. The current study uses a data-driven whole-brain approach to examine patterns of aberrant functional network connectivity (FNC) in a multi-site dataset comprising resting-state functional magnetic resonance images (rs-fMRI) from 117 individuals with FEP or EP and 130 individuals without a psychiatric disorder, as controls. Accounting for age, sex, race, head motion, and multiple imaging sites, differences in FNC were identified between psychosis and control participants in cortical (namely the inferior frontal gyrus, superior medial frontal gyrus, postcentral gyrus, supplementary motor area, posterior cingulate cortex, and superior and middle temporal gyri), subcortical (the caudate, thalamus, subthalamus, and hippocampus), and cerebellar regions. The prominent pattern of reduced cerebellar connectivity in psychosis is especially noteworthy, as most studies focus on cortical and subcortical regions, neglecting the cerebellum. The dysconnectivity reported here may indicate disruptions in cortical-subcortical-cerebellar circuitry involved in rudimentary cognitive functions which may serve as reliable correlates of psychosis.

Multi-modal deep learning from imaging genomic data for schizophrenia classification.

Background: Schizophrenia (SZ) is a psychiatric condition that adversely affects an individual's cognitive, emotional, and behavioral aspects. The etiology of SZ, although extensively studied, remains unclear, as multiple factors come together to contribute toward its development. There is a consistent body of evidence documenting the presence of structural and functional deviations in the brains of individuals with SZ. Moreover, the hereditary aspect of SZ is supported by the significant involvement of genomics markers. Therefore, the need to investigate SZ from a multi-modal perspective and develop approaches for improved detection arises. Methods: Our proposed method employed a deep learning framework combining features from structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), and genetic markers such as single nucleotide polymorphism (SNP). For sMRI, we used a pre-trained DenseNet to extract the morphological features. To identify the most relevant functional connections in fMRI and SNPs linked to SZ, we applied a 1-dimensional convolutional neural network (CNN) followed by layerwise relevance propagation (LRP). Finally, we concatenated these obtained features across modalities and fed them to the extreme gradient boosting (XGBoost) tree-based classifier to classify SZ from healthy control (HC). Results: Experimental evaluation on clinical dataset demonstrated that, compared to the outcomes obtained from each modality individually, our proposed multi-modal approach performed classification of SZ individuals from HC with an improved accuracy of 79.01%. Conclusion: We proposed a deep learning based framework that selects multi-modal (sMRI, fMRI and genetic) features efficiently and fuse them to obtain improved classification scores. Additionally, by using Explainable AI (XAI), we were able to pinpoint and validate significant functional network connections and SNPs that contributed the most toward SZ classification, providing necessary interpretation behind our findings.

Azvudine and mortality in patients with coronavirus disease 2019: A retrospective cohort study.

OBJECTIVES: Several studies have found that azvudine (FNC) can inhibit severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication both in vivo and in vitro. However, the effect of FNC on the risk of death in patients with coronavirus disease 2019 (COVID-19) is unclear. This study aims to investigate the effect of FNC on the risk of death in patients with coronavirus disease 2019 (COVID-19). METHODS: Charts of consecutive patients hospitalized at five hospitals in Chongqing with confirmed COVID-19. The primary outcome of the study was 28-day mortality. Secondary outcomes were: ICU admission rates, length of hospital and ICU stay, and also the range of mechanical ventilation days when admission. We compared primary outcome in patients who received FNC with those in patients who did not, using a multivariable model with inverse probability weighting according to the propensity score. RESULTS: We included 1,110 patients in our study cohort. Of the 236 patients treated with FNC, 30 died within 28 days (12.7%), and of the 874 patients not treated with FNC, 206 died within 28 days (23.6%). In the crude, unadjusted analysis, a significant beneficial effect of FNC in terms of the 28-day mortality (OR 0.472, 95% CI 0.312-0.714; p < 0.001) in the overall population was detected. The adjusted odds ratio by multivariate analysis was (OR 0.498, 95% CI 0.287-0.864; p = 0.013). In the multivariate analysis with inverse probability weighting according to the propensity score, a significantly beneficial effect of FNC in terms of the 28-day mortality was further confirmed (OR 0.754, 95% CI 0.614-0.925; p = 0.007). Moreover, multivariable propensity-score analyses with matching also yielded similar results (OR 0.438, 95% CI 0.246-0.778; p = 0.005). CONCLUSION: Our results reveal that in patients with COVID-19, FNC administration was associated with a significantly reduced 28-day mortality.

Phase III, randomized, double-blind, placebo-controlled clinical study: a study on the safety and clinical efficacy of AZVUDINE in moderate COVID-19 patients.

Background: In 2019, a highly pathogenic coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surfaced and resulted in the outbreak of coronavirus disease 2019 (COVID-19). With the aim of finding effective drugs to fight against the disease, several trials have been conducted since COVID-19 can only be considered a treatable disease, from a clinical point of view, after the availability of specific and effective antivirals. AZVUDINE (FNC), initially developed for treating HIV, is a potential treatment for COVID-19 as it has the capability to lower the patient's viral load and promote recovery. Methods: Volunteers infected with SARS-CoV-2 confirmed by reverse transcription polymerase chain reaction (RT-PCR), with good kidney and liver function, who were not using other antivirals or monoclonal antibodies were eligible. Samples from patients were assessed for viral load every 48 h during treatment using reverse transcription quantitative polymerase chain reaction (RT-qPCR) and droplet digital polymerase chain reaction (ddPCR). Results: The study's primary outcome measure was the percentage of participants showing an improvement in clinical scores, while the secondary outcome measure was the percentage of participants with a clinical outcome of cure. These measures were used to assess the safety and efficacy of FNC for treating COVID-19. In the analysis of sociodemographic variables, no significant differences were detected between patients in the FNC and the placebo group for race, age group, or sex. The results showed a potential benefit to participants who received FNC during the study, as observed in the shorter hospital stay, shorter negative conversion time of SARS-CoV-2, and a significant reduction in viral load. Furthermore, the reduction in fever and chills were significant at D1, D2, and D3. In this study, a total of 112 adverse events cases were noted, with 105 cases being categorized as non-serious and only 7 cases as serious adverse events. Conclusion: The pandemic is not being effectively controlled and is causing multiple waves of infection that require extensive medical resources. However, FNC has demonstrated potential to reduce the treatment duration of moderate COVID-19 cases, thereby saving significant medical resources. This makes FNC a promising candidate for COVID-19 treatment.Clinical trial registration: [clinicaltrials.gov], identifier [NCT04668235].

Serial viral load analysis by DDPCR to evaluate FNC efficacy and safety in the treatment of mild cases of COVID-19.

Introduction: The SARS-CoV-2 outbreak has threatened the human population globally as the numbers of reinfection cases even after large-scale vaccination. Trials have been carried out to find drugs effective in fighting the disease, as COVID-19 is being considered a treatable disease only after we have antivirals. A clinical candidate originally developed for HIV treatment, AZVUDINE (FNC), is a promising drug in the treatment of COVID-19. Methods: To predict the clinical outcome of COVID-19, we examined the course of viral load, every 48 h, by RT-PCR, and disease severity using an antiviral drug, FNC, with 281 participants. A randomized clinical trial was performed to evaluate the efficacy of FNC added to standard treatment, compared with placebo group added to standard treatment, for patients with mild COVID-19. RT-qPCR and ddPCR were applied to estimate the viral load in samples from patients. Also, the clinical improvement was evaluated as well as the liver and kidney function. Results and discussion: Notably, the FNC treatment in the mild COVID-19 patients may shorten the time of the nucleic acid negative conversion (NANC) versus placebo group. In addition, the FNC was effective in reducing the viral load of these participants. The present clinical trial results showed that the FNC accelerate the elimination of the virus in and could reduce treatment time of mild patients and save a lot of medical resources, making it a strong candidate for the outpatient and home treatment of COVID-19. Clinical trial registration: https://clinicaltrials.gov/ct2/show/NCT05033145, identifier NCT05033145.

Multi-model order spatially constrained ICA reveals highly replicable group differences and consistent predictive results from resting data: A large N fMRI schizophrenia study.

Brain functional networks identified from resting functional magnetic resonance imaging (fMRI) data have the potential to reveal biomarkers for brain disorders, but studies of complex mental illnesses such as schizophrenia (SZ) often yield mixed results across replication studies. This is likely due in part to the complexity of the disorder, the short data acquisition time, and the limited ability of the approaches for brain imaging data mining. Therefore, the use of analytic approaches which can both capture individual variability while offering comparability across analyses is highly preferred. Fully blind data-driven approaches such as independent component analysis (ICA) are hard to compare across studies, and approaches that use fixed atlas-based regions can have limited sensitivity to individual sensitivity. By contrast, spatially constrained ICA (scICA) provides a hybrid, fully automated solution that can incorporate spatial network priors while also adapting to new subjects. However, scICA has thus far only been used with a single spatial scale (ICA dimensionality, i.e., ICA model order). In this work, we present an approach using multi-objective optimization scICA with reference algorithm (MOO-ICAR) to extract subject-specific intrinsic connectivity networks (ICNs) from fMRI data at multiple spatial scales, which also enables us to study interactions across spatial scales. We evaluate this approach using a large N (N > 1,600) study of schizophrenia divided into separate validation and replication sets. A multi-scale ICN template was estimated and labeled, then used as input into scICA which was computed on an individual subject level. We then performed a subsequent analysis of multiscale functional network connectivity (msFNC) to evaluate the patient data, including group differences and classification. Results showed highly consistent group differences in msFNC in regions including cerebellum, thalamus, and motor/auditory networks. Importantly, multiple msFNC pairs linking different spatial scales were implicated. The classification model built on the msFNC features obtained up to 85% F1 score, 83% precision, and 88% recall, indicating the strength of the proposed framework in detecting group differences between schizophrenia and the control group. Finally, we evaluated the relationship of the identified patterns to positive symptoms and found consistent results across datasets. The results verified the robustness of our framework in evaluating brain functional connectivity of schizophrenia at multiple spatial scales, implicated consistent and replicable brain networks, and highlighted a promising approach for leveraging resting fMRI data for brain biomarker development.

Efficacy and safety evaluation of Azvudine in the prospective treatment of COVID-19 based on four phase III clinical trials.

Azvudine (FNC) is a synthetic nucleoside analog used to treat adult patients living with human immunodeficiency virus-1 (HIV-1) infection with high viral load. After phosphorylation, Azvudine inhibits RNA-dependent RNA polymerase, leading to the discontinuation of RNA chain synthesis in viruses. In addition, Azvudine is the first dual-target nucleoside oral drug worldwide to simultaneously target reverse transcriptase and viral infectivity factors in the treatment of HIV infection. On 9 August 2022, Azvudine was incorporated into the Guidelines for the Diagnosis and Treatment of Coronavirus Disease 2019 (version ninth) issued by the National Health Commission and the National Administration of Traditional Chinese Medicine. The recommended oral dose of Azvudine for the treatment of moderate coronavirus disease 2019 (COVID-19) is 5 mg once daily, and the duration of Azvudine treatment should not exceed 14 days. Four phase III clinical trials were performed during 2020-2022 to evaluate the efficacy and safety of Azvudine in the treatment of COVID-19. The results revealed that Azvudine could reduce nucleic acid-negative conversion time, viral load, and time to improvement in clinical conditions in patients with moderate COVID-19. In addition, Azvudine exhibited good safety and tolerance. Thereafter, Azvudine was incorporated into the Chinese guidelines and expert consensus for the treatment of COVID-19 and was highly approbated. Furthermore, Azvudine was also included in the Chinese guidelines for HIV infection.

FNC inhibits non-small cell lung cancer by activating the mitochondrial apoptosis pathway.

BACKGROUND: Previously, we published that 4'-azid-2'-deoxy-2'-fluorarabinoside (FNC), a novel cytosine nucleoside analog, has good anti-viral and anti-tumor activity. OBJECTIVE: This study aimed to further explore the role and molecular mechanism of FNC in non-small cell lung cancer (NSCLC). METHODS: FNC was tested in the NSCLC H460 cell line, the Lewis mouse model, and the H460 cell xenograft model. The effects of FNC were assessed by cell viability, transwell migration, and wound scratch analyses of cell migration and invasion. Apoptosis was assessed by flow cytometry. Proteins expression was assessed by western blot and immunohistochemistry staining (IHC). RESULTS: FNC inhibits the proliferation and metastasis of H460 cells in a time- and dose-dependent manner. FNC treatment showed efficacy and low toxicity in the Lewis mouse lung cancer model as well as in the H460 cell xenograft model. Further, FNC induced H460 cell apoptosis through the activation of the mitochondrial pathway. Notably, FNC inhibited invasion by increasing E-cadherin protein and reducing the protein expression of VEGF, MMP-2, MMP-9, and CD31. CONCLUSION: FNC inhibits NSCLC by activating the mitochondrial apoptosis pathway and regulating the expressions of multiple proteins related to cell adhesion and invasion, highlighting its potential as an NSCLC therapeutic.