Validating the ChatGPT Usage Scale: psychometric properties and factor structures among postgraduate students.

BACKGROUND: The rapid adoption of ChatGPT in academic settings has raised concerns about its impact on learning, research, and academic integrity. This study aimed to develop and validate a comprehensive ChatGPT Usage Scale specifically tailored to postgraduate students, addressing the need for a psychometrically sound instrument to assess the multidimensional nature of ChatGPT usage in higher education. METHODS: A cross-sectional survey design was employed, involving 443 postgraduate students from two Egyptian universities. The initial 39-item scale underwent Exploratory Factor Analysis (EFA) using principal component analysis with Varimax rotation. Confirmatory Factor Analysis (CFA) was conducted to assess the model fit and psychometric properties of the final 15-item measure. Internal consistency reliability was evaluated using Cronbach's alpha and McDonald's omega. RESULTS: EFA revealed a three-factor structure explaining 49.186% of the total variance: Academic Writing Aid (20.438%), Academic Task Support (14.410%), and Reliance and Trust (14.338%). CFA confirmed the three-factor structure with acceptable fit indices (χ2(87) = 223.604, p < .001; CMIN/DF = 2.570; CFI = 0.917; TLI = 0.900; RMSEA = 0.060). All standardized factor loadings were statistically significant (p < .001), ranging from 0.434 to 0.728. The scale demonstrated good internal consistency (Cronbach's α = 0.848, McDonald's ω = 0.849) and composite reliability (CR = 0.855). The average variance extracted (AVE) was 0.664, supporting convergent validity. CONCLUSIONS: The validated ChatGPT Usage Scale provides a reliable and valid instrument for assessing postgraduate students' engagement with ChatGPT across multiple dimensions. This tool offers valuable insights into AI-assisted academic practices, enabling more nuanced investigations into the effects of ChatGPT on postgraduate education.

Aspergillus fumigatus conidial surface-associated proteome reveals factors for fungal evasion and host immunity modulation.

Aspergillus fumigatus causes aspergillosis and relies on asexual spores (conidia) for initiating host infection. There is scarce information about A. fumigatus proteins involved in fungal evasion and host immunity modulation. Here we analysed the conidial surface proteome of A. fumigatus, two closely related non-pathogenic species, Aspergillus fischeri and Aspergillus oerlinghausenensis, as well as pathogenic Aspergillus lentulus, to identify such proteins. After identifying 62 proteins exclusively detected on the A. fumigatus conidial surface, we assessed null mutants for 42 genes encoding these proteins. Deletion of 33 of these genes altered susceptibility to macrophage, epithelial cells and cytokine production. Notably, a gene that encodes a putative glycosylasparaginase, modulating levels of the host proinflammatory cytokine IL-1ß, is important for infection in an immunocompetent murine model of fungal disease. These results suggest that A. fumigatus conidial surface proteins are important for evasion and modulation of the immune response at the onset of fungal infection.

Genomic Discovery and Structure-Activity Exploration of a Novel Family of Enzyme-Activated Covalent Cyclin-Dependent Kinase Inhibitors.

Fungi have historically been the source of numerous important medicinal compounds, but full exploitation of their genetic potential for drug development has been hampered in traditional discovery paradigms. Here we describe a radically different approach, top-down drug discovery (TD3), starting with a massive digital search through a database of over 100,000 fully genomicized fungi to identify loci encoding molecules with a predetermined human target. We exemplify TD3 by the selection of cyclin-dependent kinases (CDKs) as targets and the discovery of two molecules, 1 and 2, which inhibit therapeutically important human CDKs. 1 and 2 exhibit a remarkable mechanism, forming a site-selective covalent bond to the CDK active site Lys. We explored the structure-activity relationship via semi- and total synthesis, generating an analog, 43, with improved kinase selectivity, bioavailability, and efficacy. This work highlights the power of TD3 to identify mechanistically and structurally novel molecules for the development of new medicines.

Multiple roles for hypoxia inducible factor 1-alpha in airway epithelial cells during mucormycosis.

During pulmonary mucormycosis, inhaled sporangiospores adhere to, germinate, and invade airway epithelial cells to establish infection. We provide evidence that HIF1α plays dual roles in airway epithelial cells during Mucorales infection. We observed an increase in HIF1α protein accumulation and increased expression of many known HIF1α-responsive genes during in vitro infection, indicating that HIF1α signaling is activated by Mucorales infection. Inhibition of HIF1α signaling led to a substantial decrease in the ability of R. delemar to invade cultured airway epithelial cells. Transcriptome analysis revealed that R. delemar infection induces the expression of many pro-inflammatory genes whose expression was significantly reduced by HIF1α inhibition. Importantly, pharmacological inhibition of HIF1α increased survival in a mouse model of pulmonary mucormycosis without reducing fungal burden. These results suggest that HIF1α plays two opposing roles during mucormycosis: one that facilitates the ability of Mucorales to invade the host cells and one that facilitates the ability of the host to mount an innate immune response.

Brilacidin, a novel antifungal agent against Cryptococcus neoformans.

Cryptococcus neoformans causes cryptococcosis, one of the most prevalent fungal diseases, generally characterized by meningitis. There is a limited and not very effective number of drugs available to combat this disease. In this manuscript, we show the host defense peptide mimetic brilacidin (BRI) as a promising antifungal drug against C. neoformans. BRI can affect the organization of the cell membrane, increasing the fungal cell permeability. We also investigated the effects of BRI against the model system Saccharomyces cerevisiae by analyzing libraries of mutants grown in the presence of BRI. In S. cerevisiae, BRI also affects the cell membrane organization, but in addition the cell wall integrity pathway and calcium metabolism. In vivo experiments show BRI significantly reduces C. neoformans survival inside macrophages and partially clears C. neoformans lung infection in an immunocompetent murine model of invasive pulmonary cryptococcosis. We also observed that BRI interacts with caspofungin (CAS) and amphotericin (AmB), potentiating their mechanism of action against C. neoformans. BRI + CAS affects endocytic movement, calcineurin, and mitogen-activated protein kinases. Our results indicate that BRI is a novel antifungal drug against cryptococcosis. IMPORTANCE: Invasive fungal infections have a high mortality rate causing more deaths annually than tuberculosis or malaria. Cryptococcosis, one of the most prevalent fungal diseases, is generally characterized by meningitis and is mainly caused by two closely related species of basidiomycetous yeasts, Cryptococcus neoformans and Cryptococcus gattii. There are few therapeutic options for treating cryptococcosis, and searching for new antifungal agents against this disease is very important. Here, we present brilacidin (BRI) as a potential antifungal agent against C. neoformans. BRI is a small molecule host defense peptide mimetic that has previously exhibited broad-spectrum immunomodulatory/anti-inflammatory activity against bacteria and viruses. BRI alone was shown to inhibit the growth of C. neoformans, acting as a fungicidal drug, but surprisingly also potentiated the activity of caspofungin (CAS) against this species. We investigated the mechanism of action of BRI and BRI + CAS against C. neoformans. We propose BRI as a new antifungal agent against cryptococcosis.

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus.

Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population but also children. It is prevalent to control this problem, as diabetes can lead to many health complications. As evolution happens, humankind starts integrating computer technology with the healthcare system. The utilization of artificial intelligence assists healthcare to be more efficient in diagnosing diabetes patients, better healthcare delivery, and more patient eccentric. Among the advanced data mining techniques in artificial intelligence, stacking is among the most prominent methods applied in the diabetes domain. Hence, this study opts to investigate the potential of stacking ensembles. The aim of this study is to reduce the high complexity inherent in stacking, as this problem contributes to longer training time and reduces the outliers in the diabetes data to improve the classification performance. In addressing this concern, a novel machine learning method called the Stacking Recursive Feature Elimination-Isolation Forest was introduced for diabetes prediction. The application of stacking with Recursive Feature Elimination is to design an efficient model for diabetes diagnosis while using fewer features as resources. This method also incorporates the utilization of Isolation Forest as an outlier removal method. The study uses accuracy, precision, recall, F1 measure, training time, and standard deviation metrics to identify the classification performances. The proposed method acquired an accuracy of 79.077% for PIMA Indians Diabetes and 97.446% for the Diabetes Prediction dataset, outperforming many existing methods and demonstrating effectiveness in the diabetes domain.

Ibrexafungerp is efficacious in a neutropenic murine model of pulmonary mucormycosis as monotherapy and combined with liposomal amphotericin B.

Ibrexafungerp (formerly SCY-078) is the first member of the triterpenoid class that prevents the synthesis of the fungal cell wall polymer ß-(1,3)-D-glucan by inhibiting the enzyme glucan synthase. We evaluated the in vivo efficacy of ibrexafungerp against pulmonary mucormycosis using an established murine model. Neutropenic mice were intratracheally infected with either Rhizopus delemar or Mucor circinelloides. Treatment with placebo (diluent control), ibrexafungerp (30 mg/kg, PO BID), liposomal amphotericin B (LAMB 10 mg/kg IV QD), posaconazole (PSC 30 mg/kg PO QD), or a combination of ibrexafungerp plus LAMB or ibrexafungerp plus PSC began 16 h post-infection and continued for 7 days for ibrexafungerp or PSC and through day 4 for LAMB. Ibrexafungerp was as effective as LAMB or PSC in prolonging median survival (range: 15 days to >21 days) and enhancing overall survival (30%-65%) vs placebo (9 days and 0%; P < 0.001) in mice infected with R. delemar. Furthermore, median survival and overall percent survival resulting from the combination of ibrexafungerp plus LAMB were significantly greater compared to all monotherapies (P ≤ 0.03). Similar survival results were observed in mice infected with M. circinelloides. Monotherapies also reduce the lung and brain fungal burden by ~0.5-1.0log10 conidial equivalents (CE)/g of tissue vs placebo in mice infected with R. delemar (P < 0.05), while a combination of ibrexafungerp plus LAMB lowered the fungal burden by ~0.5-1.5log10 CE/g compared to placebo or any of the monotherapy groups (P < 0.03). These results are promising and warrant continued investigation of ibrexafungerp as a novel treatment option against mucormycosis.

Efficacy of an oral lipid nanocrystal formulation of amphotericin B (MAT2203) in the neutropenic mouse model of pulmonary mucormycosis.

Invasive mucormycosis (IM) is associated with high mortality and morbidity. MAT2203 is an orally administered lipid nanocrystal formulation of amphotericin B, which has been shown to be safe and effective against other fungal infections. We sought to compare the efficacy of MAT2203 to liposomal amphotericin B (LAMB) treatment in a neutropenic mouse model of IM due to Rhizopus arrhizus var. delemar or Mucor circinelloides f. jenssenii DI15-131. In R. arrhizus var. delemar-infected mice, 15 mg/kg of MAT2203 qd was as effective as 10 mg/kg of LAMB in prolonging median survival time vs placebo (13.5 and 16.5 days for MAT2203 and LAMB, respectively, vs 9 days for placebo) and enhancing overall survival vs placebo-treated mice (40% and 45% for MAT2203 and LAMB, respectively, vs 0% for placebo). A higher dose of 45 mg/kg of MAT2203 was not well tolerated by mice and showed no benefit over placebo. Similar results were obtained with mice infected with M. circinelloides. Furthermore, while both MAT2203 and LAMB treatment resulted in a significant reduction of ~1.0-2.0log and ~2.0-2.5log in Rhizopus delemar or M. circinelloides lung and brain burden vs placebo mice, respectively, LAMB significantly reduced tissue fungal burden in mice infected with R. delemar vs tissues of mice treated with MAT2203. These results support continued investigation and development of MAT2203 as a novel and oral formulation of amphotericin for the treatment of mucormycosis.

Thoracic fluid content (TFC) using electrical cardiometry versus lung ultrasound in the diagnosis of transient tachypnea of newborn.

This study aimed to evaluate TFC by EC versus lung ultrasound (LUS) findings for diagnosing and follow-up of TTN in late preterm and term neonates. This prospective observational study was conducted on 80 neonates with gestational age ≥ 34 weeks. TTN group included 40 neonates diagnosed with TTN, and no lung disease (NLD) group included 40 neonates without respiratory distress. LUS and EC were performed within the first 24 h of life and repeated after 72 h. There was a statistically significant increase in TFC in TTN group on D1 [48.48 ± 4.86 (1 KOhm-1)] compared to NLD group [32.95 ± 4.59 (1 KOhm-1)], and then significant decrease in TFC in D3 [34.90 ± 4.42 (1 KOhm-1)] compared to D1 in the TTN group. There was a significant positive correlation between both TFC and LUS with Downes' score, TTN score, and duration of oxygen therapy in the TTN group.   Conclusion: Both LUS and TFC by EC provide good bedside tools that could help to diagnose and monitor TTN. TFC showed a good correlation with LUS score and degree of respiratory distress. What is Known: • Transient tachypnea of the newborn (TTN) is the most common cause of respiratory distress in newborns. • TTN is a diagnosis of exclusion, there are no specific clinical parameters or biomarker has been identified for TTN. What is New: • Thoracic fluid content (TFC) by electrical cardiometry is a new parameter to evaluate lung fluid volume and could help to diagnose and monitor TTN and correlates with lung ultrasound score.

Efficacy of an oral lipid nanocrystal (LNC) formulation of amphotericin B (MAT2203) in the neutropenic mouse model of pulmonary mucormycosis.

Invasive mucormycosis (IM) is associated with high mortality and morbidity and commonly afflicts patients with weakened immune systems. MAT2203 is an orally administered lipid nanocrystal (LNC) formulation of amphotericin B, which has been shown to be safe and effective against other fungal infections. We sought to compare the efficacy of MAT2203 to liposomal amphotericin B (LAMB) treatment in a neutropenic mouse model of IM due to R. arrhizus var. delemar or Mucor circinelloides f. jenssenii DI15-131. Treatment with placebo (diluent control), oral MAT2203 administered as BID and QD or intravenous LAMB for 4 days, began 16 h post infection and continued for 7 and 4 days, respectively. Survival through Day +21 and tissue fungal burden of lung or brain in animals euthanized on Day +4 served as a primary and secondary endpoint, respectively. In both infection types, MAT2203 was as effective as LAMB in prolonging median survival time (MST) and enhancing overall survival vs. placebo-treated mice ( P <0.05 by Log-Rank). Furthermore, both MAT2203 and LAMB treatment resulted in significant ∼1.0-1.5-log reduction and ∼2.0-2.2-log in R. delemar or M. circinelloides lung and brain burden, vs. placebo mice, respectively. These results support the potential efficacy of oral MAT2203 as an alternative to LAMB. Continued investigation and development of this novel oral formulation of the amphotericin B for the treatment of mucormycosis is warranted.

Semi-supervised learning and bidirectional decoding for effective grammar correction in low-resource scenarios.

The correction of grammatical errors in natural language processing is a crucial task as it aims to enhance the accuracy and intelligibility of written language. However, developing a grammatical error correction (GEC) framework for low-resource languages presents significant challenges due to the lack of available training data. This article proposes a novel GEC framework for low-resource languages, using Arabic as a case study. To generate more training data, we propose a semi-supervised confusion method called the equal distribution of synthetic errors (EDSE), which generates a wide range of parallel training data. Additionally, this article addresses two limitations of the classical seq2seq GEC model, which are unbalanced outputs due to the unidirectional decoder and exposure bias during inference. To overcome these limitations, we apply a knowledge distillation technique from neural machine translation. This method utilizes two decoders, a forward decoder right-to-left and a backward decoder left-to-right, and measures their agreement using Kullback-Leibler divergence as a regularization term. The experimental results on two benchmarks demonstrate that our proposed framework outperforms the Transformer baseline and two widely used bidirectional decoding techniques, namely asynchronous and synchronous bidirectional decoding. Furthermore, the proposed framework reported the highest F1 score, and generating synthetic data using the equal distribution technique for syntactic errors resulted in a significant improvement in performance. These findings demonstrate the effectiveness of the proposed framework for improving grammatical error correction for low-resource languages, particularly for the Arabic language.

Tuning sterol extraction kinetics yields a renal-sparing polyene antifungal.

Decades of previous efforts to develop renal-sparing polyene antifungals were misguided by the classic membrane permeabilization model1. Recently, the clinically vital but also highly renal-toxic small-molecule natural product amphotericin B was instead found to kill fungi primarily by forming extramembraneous sponge-like aggregates that extract ergosterol from lipid bilayers2-6. Here we show that rapid and selective extraction of fungal ergosterol can yield potent and renal-sparing polyene antifungals. Cholesterol extraction was found to drive the toxicity of amphotericin B to human renal cells. Our examination of high-resolution structures of amphotericin B sponges in sterol-free and sterol-bound states guided us to a promising structural derivative that does not bind cholesterol and is thus renal sparing. This derivative was also less potent because it extracts ergosterol more slowly. Selective acceleration of ergosterol extraction with a second structural modification yielded a new polyene, AM-2-19, that is renal sparing in mice and primary human renal cells, potent against hundreds of pathogenic fungal strains, resistance evasive following serial passage in vitro and highly efficacious in animal models of invasive fungal infections. Thus, rational tuning of the dynamics of interactions between small molecules may lead to better treatments for fungal infections that still kill millions of people annually7,8 and potentially other resistance-evasive antimicrobials, including those that have recently been shown to operate through supramolecular structures that target specific lipids9.

A phylogenetic approach to explore the Aspergillus fumigatus conidial surface-associated proteome and its role in pathogenesis.

Aspergillus fumigatus, an important pulmonary fungal pathogen causing several diseases collectively called aspergillosis, relies on asexual spores (conidia) for initiating host infection. Here, we used a phylogenomic approach to compare proteins in the conidial surface of A. fumigatus, two closely related non-pathogenic species, Aspergillus fischeri and Aspergillus oerlinghausenensis, and the cryptic pathogen Aspergillus lentulus. After identifying 62 proteins uniquely expressed on the A. fumigatus conidial surface, we assessed null mutants for 42 genes encoding conidial proteins. Deletion of 33 of these genes altered susceptibility to macrophage killing, penetration and damage to epithelial cells, and cytokine production. Notably, a gene that encodes glycosylasparaginase, which modulates levels of the host pro-inflammatory cytokine IL-1ß, is important for infection in an immunocompetent murine model of fungal disease. These results suggest that A. fumigatus conidial surface proteins and effectors are important for evasion and modulation of the immune response at the onset of fungal infection.

Mucormycosis in 2023: an update on pathogenesis and management.

Mucormycosis (MCR) is an emerging and frequently lethal fungal infection caused by the Mucorales family, with Rhizopus, Mucor, and Lichtheimia, accounting for > 90% of all cases. MCR is seen in patients with severe immunosuppression such as those with hematologic malignancy or transplantation, Diabetes Mellitus (DM) and diabetic ketoacidosis (DKA) and immunocompetent patients with severe wounds. The recent SARS COV2 epidemy in India has resulted in a tremendous increase in MCR cases, typically seen in the setting of uncontrolled DM and corticosteroid use. In addition to the diversity of affected hosts, MCR has pleiotropic clinical presentations, with rhino-orbital/rhino-cerebral, sino-pulmonary and necrotizing cutaneous forms being the predominant manifestations. Major insights in MCR pathogenesis have brought into focus the host receptors (GRP78) and signaling pathways (EGFR activation cascade) as well as the adhesins used by Mucorales for invasion. Furthermore, studies have expanded on the importance of iron availability and the complex regulation of iron homeostasis, as well as the pivotal role of mycotoxins as key factors for tissue invasion. The molecular toolbox to study Mucorales pathogenesis remains underdeveloped, but promise is brought by RNAi and CRISPR/Cas9 approaches. Important recent advancements have been made in early, culture-independent molecular diagnosis of MCR. However, development of new potent antifungals against Mucorales remains an unmet need. Therapy of MCR is multidisciplinary and requires a high index of suspicion for initiation of early Mucorales-active antifungals. Reversal of underlying immunosuppression, if feasible, rapid DKA correction and in selected patients, surgical debulking are crucial for improved outcomes.

A phylogenetic approach to explore the Aspergillus fumigatus conidial surface-associated proteome and its role in pathogenesis.

Aspergillus fumigatus , an important pulmonary fungal pathogen causing several diseases collectively called aspergillosis, relies on asexual spores or conidia for initiating host infection. Here, we used a phylogenomic approach to compare proteins in the conidial surface of A. fumigatus , two closely related non-pathogenic species, Aspergillus fischeri and Aspergillus oerlinghausenensis , and the cryptic pathogen Aspergillus lentulus . After identifying 62 proteins uniquely expressed on the A. fumigatus conidial surface, we deleted 42 genes encoding conidial proteins. We found deletion of 33 of these genes altered susceptibility to macrophage killing, penetration and damage to epithelial cells, and cytokine production. Notably, a gene that encodes glycosylasparaginase, which modulates levels of the host pro-inflammatory cytokine IL-1ß, is important for infection in an immunocompetent murine model of fungal disease. These results suggest that A. fumigatus conidial surface proteins and effectors are important for evasion and modulation of the immune response at the onset of fungal infection.

COVID-19 Associated Rhino-Orbital-Cerebral Mucormycosis: Clinical Features, Antifungal Susceptibility, Management and Outcome in a Tertiary Hospital in Iran.

BACKGROUND: Despite the unprecedented surge in the incidence of mucormycosis in the COVID-19 era, the antifungal susceptibility patterns (ASPs) of COVID-19 associated mucormycosis (CAM) isolates have not been investigated so far and it is unclear if the high mortality rate associated with CAM is driven by decreased susceptibility of Mucorales to antifungal drugs. OBJECTIVES: To describe the clinical, mycological, outcome and in vitro ASPs of CAM cases and their etiologies from Iran. PATIENTS/METHODS: A prospective study from January 2020 to January 2022 at a referral tertiary hospital in Tehran, Iran was conducted for screening mucormycosis through histopathology and mycological methods. The identity of Mucorales isolates was revealed with ITS-panfungal PCR& sequencing and MALDI-TOF. The AS for amphotericin B, itraconazole, isavuconazole and posaconazole was cleared according to the EUCAST antifungal susceptibility testing protocol. RESULT: A total of 150 individuals were diagnosed with CAM. Males constituted 60.7% of the population. The mean age was 54.9 years. Diabetes was the leading risk factor (74.7%). The median interval between diagnosis of COVID-19 and CAM was 31 days. The recovery rate of culture was as low as 41.3% with Rhizopus arrhizus being identified as the dominant (60; 96.7%) agent. Amphotericin B (MIC50 = 0.5 µg/ml) demonstrated the highest potency against Mucorales. CONCLUSION: Majority of the cases had either diabetes, history of corticosteroid therapy or simultaneously both conditions. Accordingly, close monitoring of blood glucose should be considered. The indications for corticosteroids therapy are recommended to be optimized. Also, an anti Mucorales prophylaxis may be necessitated to be administrated in high risk individuals. Although amphotericin B was the most active agent, a higher rate of resistance to this antifungal was noted here in comparison with earlier studies on mucormycetes from non-CAM cases.

A Candida auris-specific adhesin, Scf1, governs surface association, colonization, and virulence.

Candida auris is an emerging fungal pathogen responsible for health care-associated outbreaks that arise from persistent surface and skin colonization. We characterized the arsenal of adhesins used by C. auris and discovered an uncharacterized adhesin, Surface Colonization Factor (Scf1), and a conserved adhesin, Iff4109, that are essential for the colonization of inert surfaces and mammalian hosts. SCF1 is apparently specific to C. auris, and its expression mediates adhesion to inert and biological surfaces across isolates from all five clades. Unlike canonical fungal adhesins, which function through hydrophobic interactions, Scf1 relies on exposed cationic residues for surface association. SCF1 is required for C. auris biofilm formation, skin colonization, virulence in systemic infection, and colonization of inserted medical devices.

Candida albicans stimulates formation of a multi-receptor complex that mediates epithelial cell invasion during oropharyngeal infection.

Fungal invasion of the oral epithelium is central to the pathogenesis of oropharyngeal candidiasis (OPC). Candida albicans invades the oral epithelium by receptor-induced endocytosis but this process is incompletely understood. We found that C. albicans infection of oral epithelial cells induces c-Met to form a multi-protein complex with E-cadherin and the epidermal growth factor receptor (EGFR). E-cadherin is necessary for C. albicans to activate both c-Met and EGFR and to induce the endocytosis of C. albicans. Proteomics analysis revealed that c-Met interacts with C. albicans Hyr1, Als3 and Ssa1. Both Hyr1 and Als3 are required for C. albicans to stimulate c-Met and EGFR in oral epithelial cells in vitro and for full virulence during OPC in mice. Treating mice with small molecule inhibitors of c-Met and EGFR ameliorates OPC, demonstrating the potential therapeutic efficacy of blocking these host receptors for C. albicans.

Non-targeted Screening of Natural Products from 288 Fungal Endophytes from Canadian Fruit Crops.

Many diverse species of fungi naturally occur as endophytes in plants. The majority of these fungi produce secondary metabolites of diverse structures and biological activities. Culture extracts from 288 fungi isolated from surface-sterilized blueberries, cranberries, raspberries, and grapes were analyzed by LC-HRMS/MS. Global Natural Products Social (GNPS) Molecular Networking modeling was used to investigate the secondary metabolites in the extracts. This technique increased the speed and simplicity of dereplicating the extracts, targeting new compounds that are structurally related. In total, 60 known compounds were dereplicated from this collection and seven new compounds were identified. These previously unknown compounds are targets for purification, characterization, and bioactivity testing in future studies. The fungal endophytes characterized in this study are potential candidates for providing bio-protection to the host plant with a reduced reliance on chemical pesticides.

Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey.

Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems.