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Plant-derived natural products are an important source of potential medicines and continue to have a major impact on the drug discovery process. Natural product discovery assignments provide an opportunity to present various experimental techniques to undergraduate students in introductory medicinal chemistry. This assignment was developed to serve as a meaningful hands-on exercise to introduce undergraduate Chemistry students to the common procedure in the early phase of drug discovery using inexpensive corn silk, in which the final year undergraduate student used Recycling Preparative High-Performance Liquid Chromatography to separate and purify steroids from corn silk. The student used advanced spectroscopic methods such as UV, IR, and one- and two-dimensional NMR to elucidate the structures of these steroids. The cytotoxic activity of these steroids was also evaluated and found to be non-cytotoxic to normal human MRC-5 cells at 60 µg/mL via 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. With this assignment, students gain meaningful multidisciplinary hands-on exercises with the common procedure in the early stages of drug discovery and understand the process of isolating natural compounds that may lead to the development of new drugs.

Abstract The identification and quantification of soil heavy metal (HM) pollution sources and the identification of driving factors is a prerequisite of soil pollution control. In this paper, the Sabaochaqu Basin of the Tuotuo River, located in the Tibetan Plateau and the headwater of the Yangtze River, was selected as the study area. The soil pollution was evaluated using geochemical baseline, and the source apportionment of soil HMs was performed using absolute principal component score-multiple linear regression (APCS-MLR), edge analysis (UNMIX) and positive matrix decomposition (PMF). The driver of the source factor was identified with the geodetector method (GDM). The results of pollution evaluation showed that the HM pollution of soil in the study area was relatively light. By comparison, UNMIX model was considered to be the preferred model for soil HMs quantitative distribution in this study area, followed by PMF model. The UNMIX model results show that source 1 (U-S1) was dominated by As, with a contribution rate of 53.31%; source 2 (U-S2) was dominated by Cd and Zn, whose contribution rates are 50.35% and 46.60% respectively; source 3 (U-S3) was dominated by Pb, with a contribution rate of 45.58%; source 4 (U-S4) was dominated by Cr, Cu, Hg and Ni, with contribution rates of 60.58%, 60.07%, 51.58% and 56.45%, respectively. The GDM results showed that the main driving factors of U-S1 were distance from lake (explanatory power q = 0.328) and distance from wind channel (q = 0.168), which were defined as long-distance migration sources. The main driving factors of U-S2 were parent material type (q = 0.269) and distance from Tuotuo river (q = 0.213), which were defined as freeze-thaw sources. The main driving factors of U-S3 were distance from town (q = 0.255) and distance from county road (Yanya Line) (q = 0.221), which were defined as human activity sources. The main drivers of U-S4 were V (q = 0.346) and Sc (q = 0.323), which were defined as natural sources. The GDM results of the 3 models were generally consistent with the analytical results of similar types of sources, especially the results of PMF model and Unmix model can basically verify each other. The research results can provide important theoretical reference for the analysis of HM sources in the soil of high-cold and high-altitude regions.

Background and aim: A critical causative factor of oxidative stress and inflammation leading to several skin complications is ultraviolet-B (UVB) irradiation. Lignosus rhinocerus (LR), or tiger milk mushroom, is native to Southeast Asia. Cold water extract of an LR cultivar, TM02® (xLr®) is a promising anti-oxidant and anti-inflammatory source. However, the effects of xLr® on UVB-induced photoaging have never been elucidated. Experimental procedure: This study investigated the protective effects of xLr® and its high, medium, and low molecular weight (HLR, MLR, and LLR, respectively) fractions against UVB irradiation using in vivo Caenorhabditis elegans (C. elegans) model. Results and conclusion: The investigation revealed a significant lifespan extension of xLr® and its fractions in UVB-irradiated C. elegans, which could be mediated by the regulation of genes associated with anti-oxidant (daf-16 and sod-3) and apoptosis (cep-1, hus-1, ced-13, and egl-1) pathways. xLr® significantly reduced the ROS production in C. elegans and increased the DAF-16 nuclear translocation compared to untreated worms. Additionally, the SOD-3 expression was increased in the xLr®-treated worms. Hence, it suggests that the different components in xLr® work synergistically to protect against UVB irradiation. Our findings may be beneficial for the application of xLr® as a treatment against UVB-induced cellular damage and photoaging.

Abstract It is not clear that the genes involved with flavonoids synthesis, regulation and transport in Ilex asprella. Transcriptome analysis of leaf, stem and root has uncovered 28,478 differentially expressed genes (DEGs) that are involved in various biological processes. Among these, the expression of 31 candidate synthetase genes, 19 transcription factors, and 5 transporters associated with flavonoid biosynthesis varies across tissues, encompassing seven complete biosynthetic pathways (stilbene, aurone, flavone, isoflavone, flavonol, phlobaphene, and anthocyanin) and one partial pathway (proanthocyanidin). Tissue-specific expression patterns suggest that the stilbenes, aurones, flavones and anthocyanin branches are more prominent in roots, as indicated by key genes such as STS(Ilex_044726), CH4ʹGT(Ilex_047989), FNS(Ilex_043640) and UFGT(Ilex_014720). In leaves, the phlobaphenes and flavonols branches are dominant, determined by CHI(Ilex_005941), FNR(Ilex_039777) and FLS(Ilex_046424). The isoflavone pathway appears to be more active in stems due to the presence of IFS(Ilex_029360), mirroring the accumulation of the intermediate metabolite chalcone, which is regulated by CHS(Ilex_047537). The absence of LAR genes implies that gallocatechin, and catechin liked proanthocyanidins cannot be synthesized in I. asprella. Meanwhile, the general phenylpropanoid pathway is more active in roots, stems than in leaves, as evidenced by the expression of PAL(Ilex_042231, Ilex_014816), C4H(Ilex_017598), and 4CL(Ilex_042033). Flavanone, dihydroflavonol and leucoanthocyanidin, key intermediates, accumulate more rapidly in stem, stem and root, respectively, regulated by CHI(Ilex_005941), F3H(Ilex_004635) and DFR(Ilex_004771). Correlation and network analyses reveal that candidate regulators and transporters are closely associated with the synthesis genes. The study provides profound snoop into flavonoids metabolism in I. asprella and offers valuable refer for medicinal plant.

Abstract Pilots are considered the final line of defense for aviation safety. Before becoming a pilot, an ab initio pilot must undergo systematic flight training. This study included 25 male flying cadets. Kendall’s coefficient of concordance was used to measure the regional homogeneity of the time series of a given voxel with its 26 nearest neighboring voxels. This operation was performed for all voxels to generate a regional homogeneity map for each participant based on Kendall’s coefficient of concordance. A partial correlation analysis was performed to examine the relationship between regional homogeneity maps and flight training hours. We found that the anterior cingulate cortex in the ab initio group was significantly positively correlated with flight hours. These results suggest a potential relationship between flight training experience and the functional properties of the anterior cingulate cortex.

Abstract Reliability technology plays a significant role in ensuring the safe operation of the subsea control system. To perform a comprehensive analysis of the reliability of complex systems, a combination of Failure Mode and Effects Analysis (FMEA) and Fuzzy Fault Tree Approach (FFTA) is introduced. Firstly, the FMEA method is used to analyze the potential failure modes and causes of system failure by completing the qualitative analysis of system reliability from the perspective of multi-factor failure modes. And the risk matrix diagram is applied to determine the degree of harm of different failure modes to the system. Then, the system reliability is quantitatively analyzed using FFTA, and a fault tree model is established by dividing the system into "system-subsystem-component" and solving for the minimum cut set. In addition, the failure probability of the top-level event is quantitatively calculated by introducing fuzzy set theory, and the probabilistic importance of the bottom-level event is analyzed to find out the weak points of each subsystem. Finally, a qualitative and quantitative reliability analysis is conducted by using FMEA-FFTA method for subsea control system. Effective measures should be taken to focus on preventive protection and regular testing for the high risk, medium–high risk and medium risk modes for subsea control system.

Abstract Radix Astragali (RA) has been recognized for its therapeutic potential in allergic rhinitis (AR), yet its potential pharmacological mechanisms remain elusive. This study systematically investigated the physicochemical properties and biological activities of RA’s phytochemicals, aiming to elucidate their targets and mechanisms in AR treatment. We identified 775 potential targets of RA’s key phytochemicals and intersected these with 29,544 AR-related disease targets, pinpointing 747 shared therapeutic targets. A protein-protein interaction network analysis categorized these targets into five subclusters, with TNF, NFKB1, IKBKB, NFKBIA, and CHUK emerging as central nodes. Enrichment analysis revealed their roles in inflammatory and immune responses, particularly through the NF-κB, TNF, IL-17, Toll-like receptor, and NOD-like receptor signaling pathways. Molecular docking and dynamics simulations confirmed the strong binding affinity and stability of RA’s phytochemicals to these targets. In vivo, RA intervention effectively reversed the expression of key inflammatory markers in an IL-13-induced nasal mucosa inflammation model. Our findings suggest that RA’s multitargeted approach involves the modulation of critical inflammatory pathways, highlighting its therapeutic potential.

Abstract The global rise of multidrug-resistant pathogens, particularly Methicillin-resistant Staphylococcus aureus (MRSA), has become a critical public health concern, necessitating the urgent discovery of new antimicrobial agents. Griseorhodin C, a hydroxyquinone compound isolated from Streptomyces, has demonstrated significant inhibitory effects against MRSA. In this study, we employed a comprehensive approach combining transcriptome and metabolome analyses to investigate the underlying antimicrobial mechanism of Griseorhodin C. Our findings reveal that Griseorhodin C interferes with multiple bacterial metabolic pathways, including those essential for the biosynthesis and metabolism of amino acids, purine metabolism and energy metabolism, ultimately leading to bacterial growth inhibition and cell death. Notably, Griseorhodin C showed superior inhibitory effects compared to the clinical standard, vancomycin, both in vivo and vitro. These results highlight the potential of Griseorhodin C as a promising candidate for the development of new therapeutic strategies aimed at combating MRSA infections. The study underscores the importance of exploring natural products as sources of novel antibiotics in the ongoing fight against antimicrobial resistance.

Abstract Small interfering RNAs (siRNA) technology has emerged as a promising therapeutic tool for human health conditions like cancer due to its ability to regulate gene silencing. Despite FDA-approved, their delivery remains localized and limiting their systemic use. This study used single-walled carbon nanotubes (SWNTs) functionalized with polyethylene glycolated (PEGylated) phospholipids (PL-PEG) derivatives for systemic siRNA delivery. We developed an siRNA systemic delivery vehicle (SWNT-siRNA) by conjugating SWNT functionalized with PL-PEG containing either amine (PA) or maleimide (MA). The functionalized SWNT with a lower molecular weight of PA produced the SWNT-siRNA conjugate system with the highest stability and high siRNA loading quantity. The system delivered siRNA to a panel of tumour cell lines of different organs (i.e. HeLa, H1299 and MCF-7) and a non-cancerous human embryonic kidney 293 cells (HEK293T) with high biocompatibility and low toxicity. The cellular uptake of SWNT-siRNA conjugates by epithelial cells was found to be energy dependent. Importantly, the presence of P-glycoprotein, a marker for drug resistance, did not inhibit SWNT-mediated siRNA delivery. Mouse xenograft model further confirmed the potential of SWNT-siRNA conjugates with a significant gene knock-down without signs of acute toxicity. These findings pave the way for potential gene therapy applications using SWNTs as delivery vehicles.

Abstract Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, with a universally lethal prognosis despite maximal standard therapies. Here, we present a consensus treatment protocol based on the metabolic requirements of GBM cells for the two major fermentable fuels: glucose and glutamine. Glucose is a source of carbon and ATP synthesis for tumor growth through glycolysis, while glutamine provides nitrogen, carbon, and ATP synthesis through glutaminolysis. As no tumor can grow without anabolic substrates or energy, the simultaneous targeting of glycolysis and glutaminolysis is expected to reduce the proliferation of most if not all GBM cells. Ketogenic metabolic therapy (KMT) leverages diet-drug combinations that inhibit glycolysis, glutaminolysis, and growth signaling while shifting energy metabolism to therapeutic ketosis. The glucose-ketone index (GKI) is a standardized biomarker for assessing biological compliance, ideally via real-time monitoring. KMT aims to increase substrate competition and normalize the tumor microenvironment through GKI-adjusted ketogenic diets, calorie restriction, and fasting, while also targeting glycolytic and glutaminolytic flux using specific metabolic inhibitors. Non-fermentable fuels, such as ketone bodies, fatty acids, or lactate, are comparatively less efficient in supporting the long-term bioenergetic and biosynthetic demands of cancer cell proliferation. The proposed strategy may be implemented as a synergistic metabolic priming baseline in GBM as well as other tumors driven by glycolysis and glutaminolysis, regardless of their residual mitochondrial function. Suggested best practices are provided to guide future KMT research in metabolic oncology, offering a shared, evidence-driven framework for observational and interventional studies.

Abstract Guizhou Province is one of the most important karst regions of southwest China, with 22 Primulina species. These species are highly habitat-specialized and dependent on the soils of the karst region, and many inter-species classifications are unclear. Therefore, studying the chloroplast genomes and estimating the divergence times of there species can not only provide a better understanding of interspecific relationships but also help to know the species speciation and divergence in the karst environment. So, we sequenced and assembled the chloroplast genomes of nine Primulina species (including six endemic species of Guizhou) and conducted chloroplast genome comparison analysis and phylogenetic study. The chloroplast genome structures of the nine Primulina species were quadripartite with total lengths of 152,869–153,364 base pairs (bp) and GC content of 37.55–37.64%. There are 132 functional genes annotated, respectively. A total of 375 simple sequence repeats and 375 interspersed nuclear elements were identified. The 30 highly preferred codons identified were used at similar frequencies in different species, respectively. A phylogenetic tree constructed on the basis of the 38 chloroplast genomes showed that Primulina species form a monophyletic group. Eleven mutational hotspot regions that could serve as potential molecular markers were identified, of which two regions near the 3′ and 5′ ends of the ycf1 gene were of appropriate size and can serve as molecular markers for phylogenetic studies of Primulina. The results of molecular clock analyses indicate that the three major branches of Primulina begin to diverge in the Miocene, and the number of species proliferated in the Pliocene and Pleistocene. Most of the species of Primulina in Guizhou Province were formed in the Pleistocene and rapidly diverged within a short period of time. This research study enriches the genetic resource information of Primulina and deepens the understanding of the phylogenetic relationships of the genus.

Abstract Background Chronic pain poses a clinical challenge due to its associated costly disability and treatment needs. Determining how pain transitions from acute to chronic is crucial for effective management. Upregulation of the chemokine C-X-C motif ligand 12 (CXCL12) in nociceptive pathway is associated with chronic pain. Our previous study has reported that elevated plasma CXCL12 mediates intracerebral neuroinflammation and the comorbidity of cognitive impairment in neuropathic pain, but whether it is also involved in the pathogenesis of pathologic pain has not been investigated. Methods Intravenous or intrathecal injection (i.v. or i.t.) of recombinant mouse CXCL12, neutralizing antibody (anti-CXCL12) or AMD3100 [an antagonist of its receptor C-X-C chemokine receptor type 4 (CXCR4)] was used to investigate the role of CXCL12 signaling pathway in pain chronicity. Two behavioral tests were used to examine pain changes. ELISA, immunofluorescence staining, Western blot, quantitative Real Time-PCR and Cytokine array were applied to detect the expressions of different molecules. Results We found that increased plasma CXCL12 was positively correlated with pain severity in both chronic pain patients and neuropathic pain model in mice with spared nerve injury (SNI). Neutralizing plasma CXCL12 mitigated SNI-induced hyperalgesia. A single i.v. injection of CXCL12 induced prolonged mechanical hyperalgesia and activation of the nociceptive pathway. Multiple intravenous CXCL12 caused persistent hypersensitivity, enhanced structural plasticity of nociceptors and up-regulation of the CXCL12/CXCR4 axis in the dorsal root ganglion (DRG) and spinal dorsal horn (SDH). However, intrathecal blocking of CXCL12/CXCR4 pathway by CXCL12 antibody or CXCR4 antagonist AMD3100 significantly alleviated CXCL12-induced pain hypersensitivity and pathological changes. Conclusions Our study provides strong evidence that a sustained increase in plasma CXCL12 contributes to neuropathic pain through a positive feedback loop that enhances nociceptor plasticity, and suggests that targeting CXCL12/CXCR4 axis in plasma or nociceptive pathways has potential value in regulating pain chronicity.

Mariko Siyue Koh,1,2 Sean Shao Wei Lam,3– 6 Xiaomeng Xu,7 Jun Tian Wu,3,4,8 Priyan Ratnasingham,7 Ricco Marsel,7 Marcus Eng Hock Ong,3,9 David Bruce Matchar,2,10,11 Ngiap Chuan Tan,2,12 Chian Min Loo1,2 1Department of Respiratory and Critical Care Medicine, Singapore General Hospital, Singapore, Singapore; 2Duke-NUS Medical School, National University of Singapore, Singapore, Singapore; 3Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore; 4Health Services Research Centre, SingHealth, Singapore, Singapore; 5Health Services Research Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore; 6Lee Kong Chian School of Business, Singapore Management University, Singapore, Singapore; 7Medical, GSK, Singapore, Singapore; 8NUS Yong Loo Lin School of Medicine, Singapore, Singapore; 9Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore; 10Department of Internal Medicine (General Internal Medicine), Duke University Medical School, Durham, NC, USA; 11Department of Internal Medicine, Singapore General Hospital, Singapore, Singapore; 12SingHealth Polyclinics, SingHealth, Singapore, SingaporeCorrespondence: Mariko Siyue Koh, Department of Respiratory and Critical Care Medicine, Singapore General Hospital, 20 College Road, Academia Level 3, Singapore, 169856, Singapore, Tel +65 65767862, Fax +65 62275247, Email mariko.koh.s.y@singhealth.com.sgPurpose: Patients with asthma in Singapore often have complex patient journeys, with diagnosis and management across various primary and speciality care settings. Real-world population health data is needed to identify care gaps and inform policies.Patients and Methods: This retrospective, longitudinal cohort study assessed real-world data from adults (aged ≥ 18 years) with asthma in the SingHealth Chronic Obstructive Pulmonary Disease and Asthma Data Mart, an integrated database of electronic medical records of patients who attended primary and/or speciality care clinics in the SingHealth Regional Health System 01/01/2015– 12/31/2020. Patients were indexed by first asthma diagnosis and categorized into cohorts of index year. Patient characteristics, asthma management and outcomes were described during baseline (1-year pre-index) and follow-up periods (1-year post-index).Results: Overall, 21,215 patients were included across 4 cohorts: 2016, N=12,947; 2017, N=3419; 2018, N=2816; 2019, N=2033. Most common baseline asthma medication changed from inhaled corticosteroids (ICS) alone in the 2016 cohort (32.8% [n=4252]) to ICS/long-acting β2-agonist in the 2019 cohort (33.3% [n=677]). Asthma symptom control (mean [SD] Asthma Control Test scores) improved from 2016 to 2019 during baseline (18.38 [4.93] vs 19.87 [4.56]; p< 0.001) and follow-up (18.34 [4.23] vs 21.07 [3.51]; p< 0.001). Mean (standard deviation [SD]) number of exacerbations per patient during follow-up decreased from 2016 to 2019 (1.91 [3.11] vs 0.89 [2.07]; p< 0.001). Mean (SD) number of emergency department visits per patient during follow-up decreased from 0.21 (0.75) in 2016 to 0.17– 0.18 (0.60– 0.65; p< 0.001) between 2017 and 2019.Conclusion: Health status at first asthma diagnosis improved for each succeeding cohort from 2016 to 2019, along with improvements in patient management and outcomes. This reflects greater awareness of the condition and improved use of medication and referrals in recent years, suggesting policy changes and their implementation, including promotion of disease awareness and adoption of guideline recommendations, may improve asthma outcomes in Singapore.Plain Language Summary: In Singapore, up to 4 in 100 people are living with asthma. People with asthma can experience frequent symptoms and attacks that affect day-to-day activities, create emotional stress and reduce quality of life. Asthma has a financial impact on patients and the economy, as it can lead to missed work and reduced productivity. In Singapore, managing asthma can be complex, with patients often seeing both primary care physicians and specialists from diagnosis to treatment. This study assessed trends in the care that patients with asthma in Singapore received and their outcomes over a 4-year period. Patients’ medical records from primary and specialist care were collected for 1 year before and after their asthma diagnosis to determine whether treatment changed over time, whether symptoms improved and whether use of healthcare services changed.Between 2016 and 2019, patients with asthma had fewer, less severe asthma attacks, and fewer emergency department visits over time. The treatment types also changed; in 2019 versus 2016, more patients received combination inhalers of corticosteroids with a long-acting beta 2 (β2) agonist, or other add-on treatments, which improve patients’ asthma, including symptoms. This indicates that there was an increasing trend for physicians following new guidelines for recommended asthma care and these changes in practice ensured combination inhalers were made available to patients by the government, leading to improvement in asthma outcomes.This study shows that asthma management in Singapore improved over the 4-year period, potentially through healthcare professionals taking on board improved treatment guideline recommendations and policy changes.Keywords: asthma, asthma control, exacerbations, real world, Singapore, health outcomes

Objective To summarize the clinical phenotypes and genetic mutations of children with KCNMA1 gene mutations. Methods A retrospective analysis was performed for the clinical data of ten children with heterozygous KCNMA1 gene mutations who attended Pediatric Medical Center of Peking University First Hospital from March 2013 to May 2023, and their cli-nical manifestations, cranial radiological features, and electroencephalogram (EEG) features were summarized. Results Among the ten children with KCNMA1 mutations, there were eight boys and two girls. Nine different KCNMA1 gene mutations were identified, including five missense mutations, three frame-shifting mutations, and one splice site mutation, among which there were seven de novo mutations and two inheritable mutations. Among the ten children with KCNMA1 mutation, six presented with seizures alone, three presented with seizures and paroxysmal movement disorders, and one presented with paroxysmal movement disorders alone. For the nine children with seizures, the age of onset ranged from 3 days to 1 year and 8 months, with a median age of onset of 8 months, and the types of seizures included focal seizures (six children), epileptic spasm (four children), myoclonic seizures (two children), atypical absence seizures (two children), generalized tonic-clonic seizures (two children), myoclonic-ato-nic seizures (one child), and atonic seizures (one child), with multiple types of seizures in five children. For the children who met the diagnostic criteria for epilepsy syndrome, one child was diagnosed with infantile epileptic spasms, and one was diagnosed with myoclonic-atonic epilepsy. For the four children with movement disorders, the age of onset ranged from 15 days to 1 year and 6 months, and movement disorders mainly manifested as paroxysmal non-kinesigenic dystonia, with paroxysmal movement disorders comorbid with abnormal eye movement in one child. Among the ten children with KCNMA1 mutation, there were four children with abnormal cranial MRI findings, including ventriculomegaly (three children), subarachnoid space widening (two children), dysgenesis of corpus callosum (two children), and delayed white matter myelination (one child). EEG showed slow background activity in five children, and epileptiform discharges were observed in all ten children during the interictal period, manifesting as focal discharges, multifocal discharges, or generalized discharges, with hypsarrhythmia in four children and electrical status epilepticus during sleep in one child. Seizure were observed in four children, among whom three had epileptic spasms and one had atypical absence seizures. All 10 children had developmental delay. Conclusion The phenotypic spectrum of nervous system diseases in children with KCNMA1 gene mutations mainly include epilepsy, paroxysmal non-kinesigenic dystonia, and developmental delay. Epilepsy usually occurs before the age of 2 years and has multiple seizure types, with focal seizures as the most common type. Paroxysmal movement disorders mainly manifest as paroxysmal dystonia.

Objective To investigate the relationship of A1298C (rs1801131) polymorphism in the MTHFR gene encoding methylenetetrahydrofolate reductase with the cerebrospinal fluid β-amyloid 1-42 subtype (Aβ1-42) level, cognitive level, and brain structure changes in patients with Alzheimer’s disease (AD). Methods A total of 1 271 subjects who met the inclusion criteria in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were selected. A multivariable linear regression model was used to analyze the relationship of MTHFR gene A1298C (rs1801131) polymorphism with cerebrospinal fluid Aβ1-42 level, cognitive level, and brain structure changes in AD patients. A mediation model was used to analyze the role of cerebrospinal fluid Aβ1-42 level in mediating the relationship between MTHFR gene A1298C (rs1801131) polymorphism and cognitive level. Results Multivariable linear regression model analysis showed that A1298C-C allele was negatively correlated with the level of Aβ1-42 in cerebrospinal fluid, hippocampal volume, and cognitive level (β=-0.121--0.084,t=-3.308--1.953,P

Breast cancer has become one of the most prevalent cancers among women worldwide, posing a significant burden on their health. Current standard therapies are often expensive and associated with the risk of drug resistance. Drug repurposing has gained increasing attention as a cost-effective and time-saving strategy in pharmaceutical research. Many drugs already in clinical use or undergoing clinical trials can be repurposed for the treatment of new clinical indications. Based on a comprehensive understanding of the mechanisms of action of these drugs and the pathophysiological processes of breast cancer, researchers can better identify drugs with potential anti-breast cancer properties and translate them into clinical practice. This paper provides a review of the current research on repurposing existing drugs for breast cancer treatment, summarizes the mechanisms of action of these drugs, and discusses the challenges associated with the strategy of drug repurposing.

Introduction: Since 2016, several therapies have been approved for treating atopic dermatitis (AD) in Singapore, including biologics, oral Janus kinase (JAK) inhibitors and topical crisaborole. This study supplements the 2016 Singapore treatment guidelines for AD, focusing on newer therapies for moderate-to-severe disease, while revisiting older treatment regimens to accommodate changes in knowledge and practice. Method: A modified Delphi panel was held, led by 2 co-chairs. The voting expert panel consisted of 12 dermatologists experienced in managing AD in Singapore. Delphi survey rounds were conducted between 24 July and 27 October 2023. Panellists indicated their agreement with drafted statements using a 5-point Likert scale. Consensus was defined as ≥80% agreement. An expert meeting was held to facilitate the consensus process between rounds 1 and 2 of voting. Results: All expert panellists participated in both survey rounds, with a 100% response rate. Thirty-nine statements, classified into general principles, conventional treatments, biologics and JAK inhibitors, were proposed. Of these, 27 statements reached consensus at the end of round 1. After the expert meeting, 17 statements were included in round 2, of which 16 statements reached consensus. One statement did not reach consensus. Key updates are the inclusion of dupilumab and JAK inhibitors as potential first-line treatments for moderate-to-severe AD, in certain populations. Conclusion: This modified Delphi study generated consensus among Singapore dermatology experts, to update treatment guidelines in moderate-to-severe atopic dermatitis. The consensus statements developed are intended to supplement the 2016 Singapore treatment guidelines for AD. Further revisions may be required when new evidence and/or treatments become available.

Abstract Coronary artery disease (CAD) is a predominant cardiovascular condition influenced by risk factors, with an emphasis on major depressive disorder (MDD). However, the shared mechanisms and therapeutic targets for CAD and MDD remain incompletely comprehended. Functional enrichment analyses were conducted to investigate the pathways associated with the differentially expressed genes (DEGs) in the CAD and MDD datasets. Hub genes were identified utilizing the Protein-Protein Interaction network and Cytoscape software. The single sample gene set variation analysis was applied to assess immune cell infiltration in the CAD and MDD datasets. Weighted gene co-expression network analysis and molecular biological experiments were executed to evaluate these hub genes. Molecular docking was conducted to identify drug candidates targeting these hub genes. The overlapping DEGs between the CAD and MDD datasets were mainly enriched in the Herpes simplex virus 1 infection and the NF-kappa B signaling pathways. CDC42, NDUFB3, and TXN were validated within the eigengenes of the blue module, which exhibited a significant association with the CAD phenotype. The drug candidate GS-9620 was identified as a potential protective agent against both disorders. In conclusion, CDC42, NDUFB3, and TXN held potential as molecular biomarkers and therapeutic targets for the simultaneous treatment of CAD and MDD.

Abstract Hypocalcemia is a common complication of thyroidectomy, particularly total thyroidectomy. The higher incidence of hypocalcemia following total thyroidectomy is primarily due to inadvertent damage to the parathyroid glands during surgery. This study aims to investigate the predictive value of intraoperative parathyroid hormone (IOPTH) in determining hypocalcemia during hospitalization and the recovery of parathyroid function after total thyroidectomy, as well as the factors that may influence IOPTH. In this retrospective study, we analyzed a cohort of 164 patients who underwent total thyroidectomy at our institution between 2018 and 2019. IOPTH was measured either 15 min after bilateral thyroidectomy combined with area VI lymph node dissection, or 15 min after bilateral thyroidectomy alone. We plotted ROC curves for IOPTH and ΔPTH% in relation to hypocalcemia during hospitalization and recovery of parathyroid function. Additionally, we explored risk factors for the development of hypocalcemia during hospitalization and factors that may influence IOPTH. IOPTH and ΔPTH% demonstrated good specificity and sensitivity for predicting hypocalcemia during hospitalization and for assessing recovery of parathyroid function. Lower preoperative PTH levels, chronic lymphocytic thyroiditis, and intraoperative parathyroid auto-transplantation were identified as risk factors for IOPTH

Abstract In this study, MnFe2O4 microspheres were synthesized to activate potassium persulfate complex salt (Oxone) for the degradation of 17β-estradiol (17β-E2) in aqueous solutions. The characteristic of MnFe2O4 was detected by XRD, XPS and SEM-EDS. The experimental results indicated that the degradation of 17β-E2 followed pseudo-first-order kinetics. At 25 °C, 17β-E2 concentration of 0.5 mg/L, MnFe2O4 dosage of 100 mg/L, Oxone dosage of 0.5 mmol/L, and initial pH value of 6.5, the decomposition efficiency of 17β-E2 reached 82.9% after 30 min of reaction. Additionally, free radical quenching experiments and electron paramagnetic resonance analysis demonstrated that SO4 −• and •OH participated in the reaction process of the whole reaction system, with SO4 −• being the main reactive oxygen species (ROS). The activation mechanism of the MnFe2O4/Oxone/17β-E2 system is proposed as follows: MnFe2O4 initially reacts with O2 and H2O in solution to generate active Fe3+-OH and Mn2+-OH species. Subsequently, Fe3+-OH and Mn2+-OH react with Oxone in a heterogeneous phase activation process, producing highly reactive free radicals. After four cycles of MnFe2O4 material, the removal rate of 17β-E2 decreased by 24.1%.

Abstract Background It is not clear if conventional liver fat cutoff of 5.56% weight which has been used for identifying fatty liver in western populations is also applicable for Asians. In Asian women of reproductive age, we evaluate the optimum metabolic syndrome (MetS)-linked liver fat cutoff, the specific metabolomic alterations apparent at this cutoff, as well as prospective associations of preconception liver fat levels with gestational dysglycemia. Methods Liver fat (measured by magnetic resonance spectroscopy), MetS, and nuclear magnetic resonance (NMR)-based plasma metabolomic profiles were assessed in 382 Asian women, who were planning to conceive. Ninety-eight women went on to become pregnant and received an oral glucose tolerance test at week 26 of gestation. Results The optimum liver fat cutoff for diagnosing MetS was 2.07%weight. Preconception liver fat was categorized into Low (liver fat

Abstract Background Mentoring’s success in nurturing professional identity formation (PIF) has been attributed to its ability to build personalised and enduring mentoring relationships. However, beyond functioning as communities of practice (CoPs) supporting socialisation processes, how mentoring integrates programme values and instils a shared identity amongst mentees remains unclear. The need for personalised guidance and timely attention to a mentee’s unique needs in evolving mentoring relationships point to the critical role of support mechanisms (‘mentoring support’). We conducted a systematic scoping review (SSR) studying “What is known about mentoring support’s role in nurturing PIF?”. Methods Adopting PRISMA-ScR guidelines, this SSR was guided by the Systematic Evidence-Based Approach (SEBA). Independent searches were carried out on publications featured between 1st January 2000 and 30th June 2023 in PubMed, Embase, ERIC and Scopus databases. The Split Approach saw concurrent, independent thematic and content analyses of the included articles. The Jigsaw Perspective combined complementary themes and categories, creating broader themes/categories. The subsequent Funnelling Process formed key domains that platformed the synthesis of the discussion. Results Two thousand three hundred forty-one abstracts were reviewed, 323 full-text articles were appraised and 151 articles were included and analysed. The key domains identified were (1) definitions and roles; (2) personalisation; (3) shepherding; and (4) PIF. Conclusion The success of mentoring in PIF lies in its ability to blend role modelling, supervision, mentoring, coaching and teaching, with self-care, guided reflection, apprenticeship and assessment to meet the individual needs of the mentee and their changing circumstances. Blending the contents of the mentoring umbrella emphasises the critical role of the mentor and host organisation in supporting mentor training, communications, support and assessment mechanisms. Mentee engagement and its active role in support measures complement the CoP-like mentoring programme’s use of blending mentoring support to advance the socialisation process. These insights reflect a complex interactive process scaffolding the development of mentoring relationships and PIF. The effect of the mentoring umbrella on clinical practice requires further study.

Abstract A highly selective, sensitive caffeic acid (CA) detection based on calcium oxide nanoparticles (CaO NPs) derived from extract of Moringa oleifera leaves decorated graphitic carbon nitride covalently grafted poly vinyl alcohol (CaO/g-C3N4/PVA) nanocomposite modified glassy carbon electrode (GCE) was studied. A facile sonochemical method was adapted to synthesis nanomaterials and characterized by HR-TEM (High resolution transmission electron microscopy), FT-IR (Fourier transform infrared spectroscopy), XRD (X-ray diffraction), FE-SEM (Field emission scanning electron microscopy), EDX (Energy dispersive X-ray analysis), Mapping and BET (Brunauer-Emmett-Teller) analysis, and electrochemical techniques. The nanocomposite modified GCE exhibited an excellent catalytic performance to the oxidation of CA under optimized conditions owing to better electron transfer efficiency, conductivity and high surface area of the electrode material. The present electrochemical sensor showed high selectivity towards the determination of 10 µM CA in the presence of 100-fold higher concentrations of interferents. The modified CA sensor exhibited a wide sensing linear range from 0.01 µM to 70 µM and the detection limit (LOD) was found to be 0.0024 µM (S/N = 3) in 0.1 M phosphate buffer saline (PBS) as a supporting electrolyte at pH 7.0. The fabricated CA sensor provides an excellent stability, reproducibility and selectivity for the determination of CA. The modified CA sensor was applied to real blood plasma samples and obtained good recovery (97.6-100.1%) results.

Abstract Coal mining-induced surface subsidence can significantly impact resident safety and hinder regional sustainable development, making precise subsidence monitoring and prediction critical. Existing mining subsidence monitoring technologies often exhibit low spatiotemporal resolution, while subsidence prediction models suffer from heavy dependence on data quality and model assumptions, as well as imprecise parameters. This study addresses these limitations by proposing a novel mining subsidence monitoring and prediction method based on Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the Convolutional Neural Network—Bidirectional Gated Recurrent Unit—Attention (CNN-BiGRU-Attention) model. Focusing on the Banji mining area in Anhui Province, ground subsidence was monitored from July 15, 2021, to September 3, 2023, utilizing SBAS-InSAR technology with Sentinel-1A satellite data. The monitoring results were validated using leveling measurement data. A CNN-BiGRU-Attention prediction model was subsequently constructed based on the time-series monitoring data. The results indicate that the surface subsidence rate in the study area decreases progressively from northwest to southeast, with an average subsidence rate ranging from -49.844 mm/year to -14.810 mm/year. At feature points, the CNN-BiGRU-Attention model effectively captures the characteristics of subsidence time-series changes. For regional subsidence prediction, this model maintains the smallest error, with Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values of 1.27 mm and 1.44 mm, respectively, and an absolute prediction error of less than 1 mm in most areas. This study integrates SBAS-InSAR technology with the CNN-BiGRU-Attention model to enable unmanned monitoring and prediction of mining subsidence. In comparison to traditional methods, this approach not only reduces monitoring costs but also enhances the accuracy of subsidence predictions, offering critical technical support for the sustainable development of mining areas.

Abstract The main purpose of this study was to construct two nomograms to predict all-cause mortality (ACM) and cause-specific mortality (CSM) in non-muscle-invasive bladder cancer (NMIBC) patients after transurethral resection of bladder tumors (TURBTs). We selected NMIBC patients who underwent TURBT between 2004 and 2017 from the Surveillance, Epidemiology, and End Results database. The patients were randomly divided into a training set and a validation set at a ratio of 7:3. The independent influencing factors of ACM and CSM in the training set were determined by univariate and multivariate Cox regression analyses. We then integrated those independent influencing factors to construct nomograms. These prediction nomograms were further verified in the validation set. The C-index, calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) curve were used to evaluate the identification, calibration, predictive ability and clinical effectiveness of the nomograms. A total of 28,086 cases were ultimately included in this study, which were divided into a training set (19,661 individuals) and a validation set (8425 individuals). Nine variables, including age at diagnosis, race, marital status, tumor grade, T stage, tumor size, number of tumors, and primary site, were obtained via multivariate Cox regression of the training set and used to construct two nomograms prediction model. The C-index values for the ACM nomogram were 0.743 and 0.741 for the training and validation sets, respectively. Moreover, the corresponding values of the C-index for the CSM nomogram were 0.785 and 0.786, respectively. The ROC curves, calibration curves, and DCA curves showed good predictive performance. The nomograms can assist clinicians in identifying high-risk populations and devising more individualized treatment strategies for NMIBC patients.

Abstract Infectious diseases are a global public health problem that poses a threat to human society. Since the 1970s, constantly mutated new infectious viruses have been quietly attacking humanity, and at least one new type of infectious disease is discovered every year. Therefore, early warning of infectious diseases will greatly reduce the socio-economic harm of infectious diseases. This study is based on the data of COVID-19 epidemic in China (except Macau and Taiwan Province) from 2020 to 2022. Firstly, we used ArcGIS software to analyze the spatial agglomeration pattern of the number of patients in various regions of China through global spatial autocorrelation analysis, local spatial autocorrelation analysis, center of gravity trajectory migration algorithm and other statistical tools; In addition, the areas with serious COVID-19 epidemic and requiring special attention were screened out. Then, autoregressive integrated moving average model (ARIMA), extreme learning machine (ELM), support vector regression (SVR), wavelet neural network (Wavelet), recurrent neural network (RNN) and long short-term memory (LSTM) were used to predict COVID-19 epidemic data in Guangdong Province, China; And the prediction performance of each model was compared through prediction accuracy indicators. Finally, a multi algorithm fusion learning model based on stacking technology is proposed to address the problem of poor generalization ability of single algorithm models in prediction; Furthermore, radial basis function network (RBF) was used as a two-level meta learner to fuse the above models, and particle swarm optimization (PSO) was used to optimize RBF parameters to reduce generalization error. The experimental results show that the performance of the integrated model is better than that of the single model in the COVID-19 dataset. In order to better apply the stacking model to the prediction of new infectious diseases, we applied the prediction model based on the COVID-19 dataset to the prediction of the number of AIDS and pulmonary tuberculosis (PTB) cases, and verified the wide applicability of our model in the prediction of infectious diseases.

Abstract The bean beetle (Callosobruchus maculatus) clearly exhibits population density-dependent polymorphism. Cytochrome P450 (CYP) is involved in many aspects of the physiological activities of insects. However, the role of CYP in population density-dependent polymorphisms remains unknown. The terminal oocyte maturity rate of high-population-density individuals (H) was faster than that of low-population-density individuals (L). A total of 56 CYP-like genes were identified from transcriptomic and genomic data, including seven clan 2 CYP-like genes, seven mitochondrial CYP-like genes, 19 clan 3 CYP-like genes, and 23 clan 4 CYP-like genes. Gene duplication might occur in CYP9Z4-like, CYP345A1-like, CYP345A2-like, CYP349A1-like, CYP349A2-like, and CYP4BN4-like. Thirteen and two CYP-like genes were up-regulated and down-regulated, respectively, in H. Among these CYP-like genes, CYP4BN4v7-like was the most abundant CYP. CYP4BN4v7-like was more highly expressed in the head than in the thorax and abdomen. Its mRNA levels in the head, thorax, and abdomen were greater in H than in L. After RNA interference decreased its mRNA level, the terminal oocyte maturity rate decreased. Moreover, the expression level of insulin-like peptide 1 (ILP1), which plays a vital role in regulating terminal oocyte development, decreased in the head. In conclusion, CYP4BN4v7-like modulated the population density-dependent terminal oocyte maturity rate by regulating the expression of ILP1.

Abstract Decentralized Federated Learning improves data privacy and eliminates single points of failure by removing reliance on centralized storage and model aggregation in distributed computing systems. Ensuring the integrity of computations during local model training is a significant challenge, especially before sharing gradient updates from each local client. Current methods for ensuring computation integrity often involve patching local models to implement cryptographic techniques, such as Zero-Knowledge Proofs. However, this approach becomes highly complex and sometimes impractical for large-scale models that use techniques such as random dropouts to improve training convergence. These random dropouts create non-deterministic behavior, making it challenging to verify model updates under deterministic protocols. We propose ProxyZKP, a novel framework combining Zero-Knowledge Proofs with polynomial proxy models to provide computation integrity in local training to address this issue. Each local node combines a private model for online deep learning applications and a proxy model that mediates decentralized model training by exchanging gradient updates. The multivariate polynomial nature of proxy models facilitates the application of Zero-Knowledge Proofs. These proofs verify the computation integrity of updates from each node without disclosing private data. Experimental results indicate that ProxyZKP significantly reduces computational load. Specifically, ProxyZKP achieves proof generation times that are 30–50% faster compared to established methods like zk-SNARKs and Bulletproofs. This improvement is largely due to the high parallelization potential of the univariate polynomial decomposition approach. Additionally, integrating Differential Privacy into the ProxyZKP framework reduces the risk of Gradient Inversion attacks by adding calibrated noise to the gradients, while maintaining competitive model accuracy. The results demonstrate that ProxyZKP is a scalable and efficient solution for ensuring training integrity in decentralized federated learning environments, particularly in scenarios with frequent model updates and the need for strong model scalability.

Abstract Guided by the goal of improving the heat transfer efficiency of geothermal cement, the development of high thermal conductivity fillers is urgently needed. Considering the drawbacks of carbon-based fillers in deteriorating the strength and fluidity of cement, this study proposes the use of green and low-cost deep eutectic solvents (DES) loaded with graphene oxide to form nanofluids (GONF) to improve the connection between graphene oxide and cement. The thermal conductivity and basic properties of GONF-cement under thermal storage conditions (60–100 °C, 0–36%NaCl) were reported, and the influence mechanism of nanofluids was analyzed. The following interesting conclusions were found: (1) The oxygen-containing functional groups on the surface of graphene oxide were connected to active sites of DES through the formation of the hydrogen bond network, forming thermal stable nanofluids. (2) Nanofluids was proven to be a low-cost (reduce material costs of graphene oxide by 90%) and effective material for enhancing the thermal conductivity and mechanical properties of geothermal cement while maintaining appropriate fluidity and low variation of density. (3) The synergistic mechanism between nanofluids and cement is due to the fact that nanofluids promotes hydration process by providing stable reaction sites, significantly reducing the porosity and pore size of cement. The dense structure enhances compressive strength and shortens the heat transfer path. This study enriches geothermal cementing technology and provides support for the low-cost application of graphene oxide in exploration engineering.

Abstract The current investigation proposes a novel hybrid methodology for the diagnosis of the foot fractures. The method uses a combination of deep learning methods and a metaheuristic to provide an efficient model for the diagnosis of the foot fractures problem. the method has been first based on applying some preprocessing steps before using the model for the features extraction and classification of the problem. the main model is based on a pre-trained ZFNet. The final layers of the network have been substituted using an extreme learning machine (ELM) in its entirety. The ELM part also optimized based on a new developed metaheuristic, called enhanced snow ablation optimizer (ESAO), to achieve better results. for validating the effectiveness of the proposed ZFNet/ELM/ESAO-based model, it has been applied to a standard benchmark from Institutional Review Board (IRB) and the findings have been compared to some different high-tech methods, including Decision Tree / K-Nearest Neighbour (DT/KNN), Linear discriminant analysis (LDA), Inception-ResNet Faster R-CNN architecture (FRCNN), Transfer learning‑based ensemble convolutional neural network (TL-ECNN), and combined model containing a convolutional neural network and long short-term memory (DCNN/LSTM). Final results show that using the proposed ZFNet/ELM/ESAO-based can be utilized as an efficient model for the diagnosis of the foot fractures.

Abstract The growing demand for Cyber-Physical Systems (CPS) requires strong reliability. However, implementing Design for Reliability (DfR) in CPS requires a deep understanding of the components’ Physics of Failure. In this work, an autonomous braking system, a Cyber-Physical System is chosen to demonstrate the application of failure physics for DfR of CPS. The component under investigation is a crystal oscillator in the control circuit of the braking system. By subjecting it to a temperature cycling that mimics the realistic environment of the component, its degradation is found to significantly increase vehicle stopping distances, thereby posing potential safety hazards. Importantly, the relationship between the oscillator’s degradation and the stopping distance is non-linear, which is critical in avoiding simplistic extrapolations from initial degradation data to determine the time to replace the braking system. The outcomes of this study also provide essential design guidelines to enhance the reliability and safety of autonomous vehicle braking systems.

Abstract This paper investigates the flexural bearing behavior of reinforced concrete beams through experimental analysis and advanced machine learning predictive models. The primary problem centers around understanding how varying compositions of construction materials, particularly the inclusion of recycled aggregates and carbon fiber-reinforced polymer (CFRP), affect the structural performance of concrete beams. Eight beams, including those with natural aggregates, recycled aggregates, fly ash, and CFRP, were tested. The study employs state-of-the-art machine learning frameworks, including Random Forest Regressor (RFR), XGBoost (XGB), and LightGBM (LGBM). The formation of these models involved data acquisition from experiments, preprocessing of key input features (such as rebars area, cement portion, recycled and natural aggregate masses, silica fume, fly ash, compressive strength, and CFRP presence), model selection, and hyperparameter tuning using Pareto optimization. The models were then evaluated using performance metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2). Outputs focus on load-induced deflection and mid-span displacement. With a dataset of 4851 samples, the optimized models demonstrated excellent performance. The experimental results revealed substantial enhancements in both compressive strength and load-bearing capacity, notably observed in beams incorporating 70% recycled aggregate and 10% silica fume. These beams exhibited a remarkable increase in compressive strength of up to 53.03% and a 7% boost in load-bearing capacity compared to those without recycled aggregate. By integrating experimental analysis with advanced computational techniques, this study advances the understanding of eco-friendly construction materials and their performance, shedding light on the intricate interactions between sustainable construction materials and the flexural bearing behavior of beams.

Abstract The study of denture base resin fabricated by digital technology with surface properties or color stability remains limited. In this study, thermal cycling and staining media (distilled water, artificial saliva, green tea, and Coca-Cola) immersion were used to simulate the intraoral environment to assess the surface properties and color stability of CAD/CAM (milled) and 3D-printed base resin materials, the conventionally polymerized base served as the control group. After thermal cycling, all groups showed increased surface roughness, contact angle (i.e. hydrophilicity) and color difference (∆E), the 3D-printed group had the most significant increase among the 3 groups (P

Abstract Sepsis, characterized as a systemic inflammatory response triggered by the invasion of pathogens, represents a continuum that may escalate from mild systemic infection to severe sepsis, potentially resulting in septic shock and multiple organ dysfunction syndrome. Advancements in lipidomics and metabolomics have unveiled the complex role of fatty acid metabolism (FAM) in both healthy and pathological states. Leveraging bioinformatics, this investigation aimed to identify and substantiate potential FAM-related genes (FAMGs) implicated in sepsis. The approach encompassed a differential expression analysis across a pool of 36 candidate FAMGs. GSEA and GSVA were employed to assess the biological significance and pathways associated with these genes. Furthermore, Lasso regression and SVM-RFE methodologies were implemented to determine key hub genes and assess the diagnostic prowess of nine selected FAMGs in sepsis identification. The study also investigated the correlation between these hub FAMGs. Validation was conducted through expression-level analysis using the GSE13904 and GSE65682 datasets. The study identified 13 sepsis-associated FAMGs, including ABCD2, ACSL3, ACSM1, ACSS1, ACSS2, ACOX1, ALDH9A1, ACACA, ACACB, FASN, OLAH, PPT1, and ELOVL4. As demonstrated by functional enrichment analysis results, these genes played key roles in several critical biological pathways, such as the Peroxisome, PPAR signaling pathway, and Insulin signaling pathway, all of which are intricately linked to metabolic regulation and inflammatory responses. The diagnostic potential of these FAMGs was further highlighted. In short, the expression patterns of these FAMGs c effectively distinguished sepsis cases from non-septic controls, which suggested that they may be promising biomarkers for early sepsis detection. This discovery not only enhanced our understanding of the molecular mechanisms underpinning sepsis but also paved the way for developing novel diagnostic tools and therapeutic strategies targeting metabolic dysregulation in septic patients. This research sheds light on 13 FAMGs associated with sepsis, providing valuable insights into novel biomarkers for this condition and facilitating the monitoring of its progression. These findings underscore the significance of purine metabolism in sepsis pathogenesis and open avenues for further investigation into therapeutic targets.

Abstract Background Utilising ultrasound technology has resulted in higher success and lower complication rates during regional anaesthesia (RA) procedures. Proper training is necessary to accurately identify structures, optimise images, and improve hand–eye coordination. Simulation training using immersive virtual environments and simulation models has enabled this competency training to be conducted safely before performing on patients. We conducted a study to compare the simulator performance and users’ feedback on a Blue Phantom Regional Anaesthesia Ultrasound Training Block and NeedleTrainer™. Methods Forty-seven participants were recruited via convenient sampling during a RA workshop for novice practitioners. They were divided into the N or B group and then crossover to experience using both Blue Phantom and NeedleTrainer model. Time-to-reach-target, first-pass success rate, and complication rate were assessed, while the learning and confidence scores were rated using six-item and three-item questionnaires, respectively, via a 5-point Likert scale. Results Blue Phantom model has a longer time-to-target as compared to the NeedleTrainer model (16 ± 8 vs 8 ± 3 s, p

Abstract The pathogenesis of head and neck squamous cell carcinoma (HNSCC) is notably complex. Early symptoms are often subtle, and effective early screening methods are currently lacking. The tumors associated with HNSCC develop rapidly, exhibit high aggressiveness, and respond poorly to existing treatments, leading to low survival rates and poor prognosis. Numerous studies have demonstrated that histone posttranslational modifications (HPTMs), including acetylation, methylation, phosphorylation, and ubiquitination, play a critical role in the occurrence and progression of HNSCC. Moreover, targeting histone posttranslationally modified molecules with specific drugs has shown potential in enhancing therapeutic outcomes and improving prognosis, underscoring their significant clinical value. This review aims to summarize the role of histone posttranslational modifications in the pathogenesis and progression of HNSCC and to discuss their clinical significance, thereby providing insights into novel therapeutic approaches and drug development for this malignancy.

Abstract Background The global outbreak of the coronavirus disease 2019 (COVID-19) has been enormously damaging, in which prolonged shedding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, previously 2019-nCoV) infection is a challenge in the prevention and treatment of COVID-19. However, there is still incomplete research on the risk factors that affect delayed shedding of SARS-CoV-2. Methods In a retrospective analysis of 56,878 hospitalized patients in the Fangcang Shelter Hospital (National Convention and Exhibition Center) in Shanghai, China, we compared patients with the duration of SARS-CoV-2 viral shedding > 12 days with those days

Abstract Background Mesenchymal stem cells (MSCs) have been proposed to treat osteoarthritis (OA) for many years. However, clinical outcomes have been inconsistent due to biological variation between patients, differences in tissue source and preparation of the MSCs, and type of donor (e.g. allogenic versus autologous). Here, we test the hypothesis that inconsistent clinical outcomes are related to variations in the stemness and senescence of the injected autologous adipose-derived (AD) MSCs. Methods In the prospective randomized trial, 45 knee OA patients were divided into two groups: Group 1 (n = 22) patients treated with high tibial osteotomy (HTO) alone and Group 2 (n = 23) patients treated with HTO followed by intra-articular injection of autologous AD-MSCs (HTO + AD-MSCs). MRI and X-ray were performed pre-operation and 12 months post-operation. WOMAC and VAS score were collected four times, every 6 months over a 24-month follow-up. We observed the proliferation and stemness of AD-MSCs selected from the 5 patients showing the most improvement and from the 5 patients with the least improvement, and completed further in vitro experiments including beta-galactosidase activity, reactive oxygen species and bioinformatic analysis. Results The results showed that patients treated with HTO + AD-MSCs had a significant reduction in knee OA severity as compared to patients treated with HTO alone. Moreover, we discovered that proliferation and colony forming efficiency of AD-MSCs selected from the 5 patients showing the most improvement performed significantly better than cells selected from the 5 patients with the least improvement. AD-MSCs from the patients with the most improvement also had lower amounts of senescent cells and intracellular reactive oxygen species. Conclusions Clinical outcomes of autologous AD-MSCs therapy in knee osteoarthritis are correlated with stem cell stemness and senescence. Our study highlights emerging opportunities and trends in precision medicine that could potentially improve autologous MSC-based therapies.

Abstract Neoadjuvant PD-1 inhibitor is promising in cutaneous melanoma but remains unknown in acral melanoma (AM). This phase Ib trial study (Clinicaltrials.gov NCT04197882) assessed the efficacy and safety of the combination of neoadjuvant oncolytic virus orienX010 (ori) and anti-PD-1 toripalimab (tori) for resectable AM. Thirty patients of stage III/IV received neoadjuvant therapy of ori and tori for 12 weeks before surgery, followed by adjuvant treatment with tori for 1 year. Primary endpoints were radiographic and pathological response rates, with secondary endpoints of 1- and 2-year recurrence-free survival (RFS) rates, event-free survival (EFS) rates, and safety. Twenty-seven completed surgery and tori adjuvant treatment and median follow-up was 35.7 months. Radiographic and pathological response rates were 36.7% and 77.8%, with complete response rates of 3.3% and 14.8%, 1- and 2-year RFS rates of 85.2% and 81.5%, and 1- and 2-year EFS rates of 83% and 73%, respectively. Adverse events occurred in all patients, mainly grade 1–2. There was no correlation between PET/CT evaluation and pathological response or progression-free survival/overall survival. Patients with pathological response showed tumor beds with high tertiary lymphoid structures (TLSs) and tumor-infiltrating lymphocytes (TILs). Cytokines and chemokines analysis showed the combination therapy significantly increases the secretion of proinflammatory cytokines and chemokines in both responders and non-responders. Therefore, neoadjuvant ori and tori demonstrated promising antitumor activity with high response rates and high 2-year RFS/EFS for AM with acceptable tolerability.

Objectives To investigate the serum IL-6 levels and their rate of change in predicting the mortality of critically ill patients with COVID-19 in Vietnam.Design A single-centre, cross-sectional study.Setting An Intensive Care Centre for the Treatment of Critically Ill Patients with COVID-19 in Ho Chi Minh City, Vietnam.Participants We included patients aged 18 years or older who were critically ill with COVID-19 and presented to the study centre from 30 July 2021 to 15 October 2021. We excluded patients who did not have serum IL-6 measurements between admission and the end of the first day.Primary outcome measures The primary outcome was hospital all-cause mortality.Results Of 90 patients, 41.1% were men, the median age was 60.5 years (Q1–Q3: 52.0–71.0), and 76.7% of patients died in the hospital. Elevated IL-6 levels were observed on admission (41.79 pg/mL; Q1–Q3: 20.68–106.27) and on the third day after admission (72.00 pg/mL; Q1–Q3: 26.98–186.50), along with a significant rate of change in IL-6 during that period (839.5%; SD: 2753.2). While admission IL-6 level (areas under the receiver operator characteristic curve (AUROC): 0.610 (95% CI: 0.459 to 0.761); cut-off value ≥15.8 pg/mL) and rate of change in IL-6 on the third day of admission (AUROC: 0.586 (95% CI: 0.420 to 0.751); cut-off value ≥−58.7%) demonstrated poor discriminatory ability in predicting hospital mortality, the third day IL-6 rate of change from admission ≥−58.7% (adjusted OR: 12.812; 95% CI: 2.104 to 78.005) emerged as an independent predictor of hospital mortality.Conclusions This study focused on a highly selected cohort of critically ill COVID-19 patients with a high IL-6 level and mortality rate. Despite the poor discriminatory value of admission IL-6 levels, the rate of change in IL-6 proved valuable in predicting mortality. To identify critically ill COVID-19 patients with the highest risk for mortality, monitoring the serial serum IL-6 measurements and observing the rate of change in serum IL-6 levels over time are needed.

Purpose Coronary CT angiography (CCTA) is well established for the diagnostic evaluation and prognostication of coronary artery disease (CAD). The growing burden of CAD in Asia and the emergence of novel CT-based risk markers highlight the need for an automated platform that integrates patient data with CCTA findings to provide tailored, accurate cardiovascular risk assessments. This study aims to develop an artificial intelligence (AI)-driven platform for CAD assessment using CCTA in Singapore’s multiethnic population. We will conduct a hybrid retrospective-prospective recruitment of patients who have undergone CCTA as part of the diagnostic workup for CAD, along with prospective follow-up for clinical endpoints. CCTA images will be analysed locally and by a core lab for coronary stenosis grading, Agatston scoring, epicardial adipose tissue evaluation and plaque analysis. The images and analyses will also be uploaded to an AI platform for deidentification, integration and automated reporting, generating precision AI toolkits for each parameter.Participants CCTA images and baseline characteristics have been collected and verified for 4196 recruited patients, comprising 75% Chinese, 6% Malay, 10% Indian and 9% from other ethnic groups. Among the participants, 41% are female, with a mean age of 55±11 years. Additionally, 41% have hypertension, 51% have dyslipidaemia, 15% have diabetes and 22% have a history of smoking.Findings to date The cohort data have been used to develop four AI modules for training, testing and validation. During the development process, data preprocessing standardised the format, resolution and other relevant attributes of the images.Future plans We will conduct prospective follow-up on the cohort to track clinical endpoints, including cardiovascular events, hospitalisations and mortality. Additionally, we will monitor the long-term impact of the AI-driven platform on patient outcomes and healthcare delivery.Trial registration number NCT05509010.

Background Awake prone positioning (APP) may be beneficial in patients with respiratory failure who are not receiving mechanical ventilation. Randomized controlled trials of APP have been performed during peak COVID-19 periods in unvaccinated populations, with limited data on compliance or patient acceptability. We aimed to evaluate the efficacy and acceptability of APP in a lower-middle income country in an open-label randomized controlled trial using a dedicated APP implementation team and wearable continuous-monitoring devices. Methods The trial was performed at a tertiary level hospital in Ho Chi Minh City, Vietnam, recruiting adults (≥18 years) hospitalized with moderate or severe COVID-19 and receiving supplemental oxygen therapy via nasal/facemask systems or high-flow nasal cannula (HFNC). Patients were allocated by a computer-generated random number sequence in a 1:1 ratio to standard care or APP, where a dedicated team provided bedside support. Wearable devices continuously recorded pulse oximetry and body position continuously. Our primary outcome was escalation of respiratory support within 28 days of randomization. Results Ninety-three patients were enrolled in this study between March 2022 and March 2023. Eighty (86%) patients had received ≥2 doses of SARS-CoV2 vaccine. The study was terminated early because of a reduction in the number of eligible patients. Data from 46 patients allocated to APP and 47 to standard care were available for analysis. At baseline, 19/47 (40%) patients allocated to the standard care group and 14/46 (30%) patients allocated to the APP group received HFNC. Continuous monitoring data were available for all patients monitored with wearable devices. Significantly greater mean daily APP times were achieved in those allocated to APP, however, most achieved less than the target 8 h/day. We did not detect clear differences in the primary outcome (relative risk,RR, 0.85, 95% CI 0.40-1.78, p=0.67) or secondary outcomes, including intubation rate and 28-day mortality. Patients reported prone positioning was comfortable, although almost all patients preferred supine positioning. No adverse events associated with the intervention were observed. Conclusions APP was not associated with benefit, but there was no sign of harm. Continuous monitoring with wearable devices is both feasible and acceptable for patients. In our population, achieving prolonged APP time was challenging despite a dedicated support team, and patients preferred supine positioning. Clinical Trials Registration NCT05083130.

Abstract Metastasis is the major cause of treatment failure in patients with prostate adenocarcinoma (PRAD). Diverse programmed cell death (PCD) patterns play an important role in tumor metastasis and hold promise as predictive indicators for PRAD metastasis. Using the LASSO Cox regression method, we developed PCD score (PCDS) based on differentially expressed genes (DEGs) associated with PCD. Clinical correlation, external validation, functional enrichment analysis, mutation landscape analysis, tumor immune environment analysis, and immunotherapy analysis were conducted. The role of Prostaglandin D2 Synthase (PTGDS) in PRAD was examined through in vitro experiments, single-cell, and Mendelian randomization (MR) analysis. PCDS is elevated in patients with higher Gleason scores, higher T stage, biochemical recurrence (BCR), and higher prostate-specific antigen (PSA) levels. Individuals with higher PCDS are prone to metastasis, metastasis after BCR, BCR, and castration resistance. Moreover, PRAD patients with low PCDS responded positively to immunotherapy. Random forest analysis and Mendelian randomization analysis identified PTGDS as the top gene associated with PRAD metastasis and in vitro experiments revealed that PTGDS was considerably downregulated in PRAD cells against normal prostate cells. Furthermore, the overexpression of PTGDS was found to suppress the migration, invasion, proliferationof DU145 and LNCaP cells. To sum up, PCDS may be a useful biomarker for forecasting the possibility of metastasis, recurrence, castration resistance, and the efficacy of immunotherapy in PRAD patients. Additionally, PTGDS was identified as a viable therapeutic target for the management of PRAD.

Objective To compare the detective and diagnostic ability of full field digital mammography (FFDM) alone and digital breast tomosynthesis (DBT) combined with FFDM (FFDM+DBT) in different types of lesions. Method A total of 389 patients who underwent preoperative mammography in Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from November 2021 to June 2022 were prospectively included in this study. FFDM images and DBT images were obtained from each patient in the Combo mode. All images were interpreted by senior radiologists, who have been involved in breast imaging diagnosis for more than 10 years. Using the pathological results as the gold standard, the consistency between the imaging evaluation and the gold standard was analyzed to evaluate the differences in diagnostic efficacy between FFDM and DBT (FFDM+DBT), and the differences in diagnostic efficacy in different types of lesions. Indice of diagnostic efficacy includes sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), area under curve (AUC) receiver operating characteristic curve (ROC) for diagnosing breast cancer and overall diagnostic ability between FFDM and FFDM+DBT were compared. Results The overall diagnostic capability of FFDM+DBT was significantly improved compared with FFDM alone (P

Abstract In this pilot study, we investigated the utility of handheld ultrasound-guided photoacoustic (US-PA) imaging probe for analyzing ex-vivo breast specimens obtained from female patients who underwent breast-conserving surgery (BCS). We aimed to assess the potential of US-PA in detecting biochemical markers such as collagen, lipids, and hemoglobin, and compare these findings with routine imaging modalities (mammography, ultrasound) and histopathology results, particularly across various breast densities. Twelve ex-vivo breast specimens were obtained from female patients with a mean age of 59.7 ± 9.5 years who underwent BCS. The tissues were illuminated using handheld US-PA probe between 700 and 1100 nm across all margins and analyzed for collagen, lipids, and hemoglobin distribution. The obtained results were compared with routine imaging and histopathological assessments. Our findings revealed that lipid intensity and distribution decreased with increasing breast density, while collagen exhibited an opposite trend. These observations were consistent with routine imaging and histopathological analyses. Moreover, collagen intensity significantly differed (P

Summary: Background: Metrics evaluating the functional quality of red blood cells (RBCs) must consider their role in oxygen delivery. Whereas oxygen-carrying capacity is routinely reported using haemoglobin assays, the rate of oxygen exchange is not measured, yet also important for tissue oxygenation. Since oxygen-unloading depends on the diffusion pathlength inside RBCs, cell geometry offers a plausible surrogate. Methods: We related the time-constant of oxygen-unloading (τ), measured using single-cell oxygen saturation imaging, with flow-cytometric variables recorded on a haematology analyser. Experiments compared freshly-drawn RBCs with stored RBCs, wherein metabolic run-down and spherical remodelling hinder oxygen unloading. Findings: Multivariable regression related τ to a ratio of side- and forward-scatter, referred to herein as FlowScore. FlowScore was able to distinguish, with sensitivity and specificity >80%, freshly drawn blood from blood that underwent storage-related kinetic attrition in O2-handling. Moreover, FlowScore predicted τ restoration upon biochemical rejuvenation of stored blood. Since RBC geometry and metabolic state are related, variants of FlowScore estimated [ATP] and [2,3-diphosphoglycerate]. The veracity of FlowScore was confirmed by four blood-banking systems (Australia, Canada, England, Spain). Applying FlowScore to data from the COMPARE study revealed a positive association with the time-delay from sample collection to measurement, which was verified experimentally. The LifeLines dataset revealed age, sex, and smoking among factors affecting FlowScore. Interpretation: We establish FlowScore as a widely-accessible and cost-effective surrogate of RBC oxygen-unloading kinetics. As a metric of a cellular process that is sensitive to storage and disease, we propose FlowScore as an RBC quality marker for blood-banking and haematology. Funding: See Acknowledgements.

Abstract Programmed cell death represents a precisely regulated and active cellular demise, governed by a complex network of specific genes and proteins. The identification of multiple forms of programmed cell death has significantly advanced the understanding of its intricate mechanisms, as demonstrated in recent studies. A thorough grasp of these processes is essential across various biological disciplines and in the study of diseases. Nonetheless, despite notable progress, the exploration of the relationship between programmed cell death and disease, as well as its clinical application, are still in a nascent stage. Therefore, further exploration of programmed cell death and the development of corresponding therapeutic methods and strategies holds substantial potential. Our review provides a detailed examination of the primary mechanisms behind apoptosis, autophagy, necroptosis, pyroptosis, and ferroptosis. Following this, the discussion delves into biological functions and diseases associated dysregulated programmed cell death. Finally, we highlight existing and potential therapeutic targets and strategies focused on cancers and neurodegenerative diseases. This review aims to summarize the latest insights on programmed cell death from mechanisms to diseases and provides a more reliable approach for clinical transformation.

Abstract Breast cancer is the leading cancer among women, with a significant number experiencing recurrence and metastasis, thereby reducing survival rates. This study focuses on the role of long noncoding RNAs (lncRNAs) in breast cancer immunotherapy response. We conducted an analysis involving 1027 patients from Sun Yat‐sen Memorial Hospital, Sun Yat‐sen University, and The Cancer Genome Atlas, utilizing RNA sequencing and pathology whole‐slide images. We employed unsupervised clustering to identify distinct lncRNA expression patterns and developed an AI‐based pathology model using convolutional neural networks to predict immune–metabolic subtypes. Additionally, we created a multimodal model integrating lncRNA data, immune‐cell scores, clinical information, and pathology images for prognostic prediction. Our findings revealed four unique immune–metabolic subtypes, and the AI model demonstrated high predictive accuracy, highlighting the significant impact of lncRNAs on antitumor immunity and metabolic states within the tumor microenvironment. The AI‐based pathology model, DeepClinMed‐IM, exhibited high accuracy in predicting these subtypes. Additionally, the multimodal model, DeepClinMed‐PGM, integrating pathology images, lncRNA data, immune‐cell scores, and clinical information, showed superior prognostic performance. In conclusion, these AI models provide a robust foundation for precise prognostication and the identification of potential candidates for immunotherapy, advancing breast cancer research and treatment strategies.

Objective This systematic review and meta-analysis aims to evaluate the effectiveness and safety of Angio-Seal and StarClose vascular closure devices (VCDs) in achieving hemostasis after interventional surgery. Methods Randomized controlled trials (RCTs) and observational studies comparing Angio-Seal and StarClose were identified through systematic searches. Data on vascular closure success rate, complication rates, hematoma formation, pseudoaneurysm, arterial occlusion, and surgical intervention were extracted and pooled using a random effects model. Results Nine studies met the inclusion criteria, total 5,466 patients. The meta-analysis revealed a slight, statistically significant difference in the success rate of vascular closure in favor of Angio-Seal (risk ratio (RR) 1.05, 95% confidence interval (CI) [1.00–1.09], p = 0.03). No significant difference was found in the overall complication rate, hematoma formation, pseudoaneurysm, arterial occlusion, or surgical intervention. Conclusions Angio-Seal demonstrated a slightly higher success rate in vessel closure compared to StarClose. Both devices had a comparable safety profile with no significant differences in major complications.