Current Medicinal Chemistry - Online First

Many medications associated with an increased risk of dementia do not have adequate warning labels, leading to a significant underestimation of their potential dangers. This study aims to leverage the FAERS database to identify drugs strongly linked to dementia and to examine the relationship between these drugs using Mendelian randomization techniques. The ultimate goal is to mitigate the risk of developing dementia.

We utilized the FAERS database to identify medications significantly associated with dementia cases. The DrugBank, OpenTargets, and STITCH databases were employed to pinpoint the target genes of these drugs. We then conducted Mendelian randomization analysis to explore the correlation between the expression of drug target genes and the incidence of dementia. Additionally, a time-to-onset analysis assessed the temporal relationships of drug ingestions. Furthermore, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Protein-Protein Interaction Network (PPI) analyses were performed to investigate the molecular pathways linked to target genes related to drugs associated with dementia.

A total of 28,139 dementia events were recorded in the FAERS database. Our Mendelian randomization analysis revealed a significant association between the expression of all identified drug target genes and dementia in both blood and brain tissues. Specifically, we identified nine drug target genes with significant correlations, implicating quetiapine, clozapine, valproic acid, alendronate, and digoxin as being strongly associated with dementia, which could provide insight into areas of clinical concern regarding dementia occurrence.

The adverse event data sourced from the FAERS database indicate that certain medications are associated with an increased risk of developing dementia, a finding corroborated by our Mendelian randomization analysis. Establishing a comprehensive monitoring and risk assessment program is crucial for identifying high-risk individuals and facilitating informed medication choices, thereby potentially reducing the incidence of dementia.

Tyrosinase, a copper-containing enzyme, is responsible for melanin production, and its overactivity can lead to hyperpigmentation.

This study aimed to evaluate triazolothiadiazoles (3a-h, 4a-f) and triazolothiadiazines (5a-h) against human and mushroom tyrosinase isozymes.

Several derivatives, such as 3a-3b, 3d, 4c-4f, 5d, and 5e, were identified as potent and selective inhibitors of mushroom tyrosinase, with IC50 values ranging from 1.9 to 15.2 µM. Similarly, compounds 3f, 4b, 5a, and 5b effectively inhibited human tyrosinase, with IC50 values between 12.6 and 18.5 µM. Mechanism-based studies revealed that these active compounds exhibited competitive inhibition against both isozymes without any cytotoxic effects. In-silico analysis further demonstrated that these compounds fit well into the active site of both tyrosinase isozymes.

Additionally, the pharmacokinetic profile of these compounds highlighted promising drug-like properties, making them potential candidates for the development of effective therapeutics for skin disorders.

Gastroesophageal reflux disease (GERD) is a prevalent digestive disorder, yet the causal roles of modifiable risk factors remain unclear. This study aims to investigate the causal relationships between 28 modifiable risk factors (including obesity traits, mental health disorders, sleep traits, metabolic comorbidities, and serum parameters) and GERD using two-sample Mendelian randomization (MR). Gastroesophageal reflux disease (GERD). Our findings aim to inform targeted prevention and treatment strategies for GERD.

This study obtained data from extensive genome-wide association studies (GWAS). Pooled data associated with gastroesophageal reflux associations were obtained from the 23andMe Research team’s research, which included a total of 129,080 cases of gastroesophageal reflux and 473,524 controls of European ancestry. We conducted a univariable Mendelian randomization (MR) analysis to ascertain whether genetic evidence of exposure demonstrated a statistically significant association with the risk of GERD. Subsequently, a multivariable MR analysis was carried out to estimate the independent effects of the exposures on GERD.

Univariable MR analysis utilizing extensive GWAS data suggested that genetic factors such as BMI, Waist circumference, Arm fat mass (left and right), Leg fat mass (left and right), Attention Deficit and Hyperactivity Disorder (ADHD), Major Depressive Disorder (MDD), Schizophrenia, Negative emotions (including nervousness, anxiety, tension, or depression), Insomnia, Sleep apnea syndrome, Sleep duration, and Snoring, as well as Total cholesterol levels and Apolipoprotein B levels, are associated with the development of GERD. Multivariate Mendelian randomization of BMI and Negative emotion as correction factors showed that Waist circumference, Arm fat mass (left and right), Leg fat mass (left and right), ADHD, Insomnia, Sleep apnea syndrome, and Snoring were associated with an increased risk of GERD (p< 0.05). Conversely, longer sleep duration was associated with a reduced risk of GERD (p< 0.05).

This MR study reveals novel causal mechanisms in GERD pathogenesis: (1) Peripheral adiposity (arm/leg fat mass) exerts independent effects beyond central obesity, indicating site-specific fat distribution significance; (2) ADHD emerges as a distinct psychiatric risk factor independent of mental disorders; (3) Sleep apnea operates through BMI-independent pathways. Collectively, these findings redefine GERD pathophysiology, highlighting fat depot specificity and brain-gut interactions as critical mechanistic drivers.

Overall, our findings suggest that multiple risk factors are associated with the risk of GERD. These results provide a theoretical basis for controlling body weight and plasticity, improving sleep habits, and preventing and timely seeking medical attention to reduce the occurrence of psychiatric disorders, which will be important strategies to prevent and alleviate GERD.

Seven new 4-[2-(dichloromethyl)pyrazolo[1,5-a][1,3,5]triazine derivatives were investigated for anticancer activity, possible molecular mechanisms of anticancer action, and ADMET properties.

The 4-benzenesulfonamide derivatives of pyrazolo[1,5-a][1,3,5]triazine were synthesized using the condensation of N-(2,2-dichloro-1-cyanovinyl)-amides IV with 1H-pyrazol-5-amine. Compound antitumor activities were evaluated using the NCI-60 human cancer cell line. AutoDockTools and AutoDock Vina software were used for molecular modeling. Using the ADMETlab 3.0 and pkCSM web sources, the ADMET properties of compounds 4, 5, and 7 were calculated.

Seven new pyrazolo[1,5-a][1,3,5]triazine derivatives were synthesized. The compounds 4, 5, and 7 exhibit high activity >1 µM against leukemia, colon, and renal cancer. Compound 4 exhibited the most potent activity, with IC50 values of 0.32  µM against leukemia, 0.49-0.89  µM against colon cancer, and 0.92  µM against renal cancer. Molecular modeling has demonstrated a potential antitumor mechanism involving CDK. The predicted ADMET profile of compounds 4, 5, and 7 is favorable.

The seven novel pyrazolo[1,5-a][1,3,5]triazines, as purine bioisosteres, were developed, synthesized, and investigated by in vitro and in silico methods.

Seven novel pyrazolo[1,5-a][1,3,5]triazine derivatives exhibited anticancer activity against the NCI-60 cancer cell lines. The compounds 4, 5, and 7 demonstrated strong anticancer activity, with growth inhibition (GI) values exceeding 50% across all nine cancer types tested. The most active compound, 4, is against leukemia, colon cancer, renal cancer, and lung cancer. All compounds exhibit low toxicity, with LC50 values of 100 µM or greater. The molecular docking of compounds 4, 5, and 7 revealed the potential to inhibit cancer-associated cyclin-dependent kinases. The predicted ADMET profiles of their compounds are favorable, providing a basis for further improvement of their anticancer activity.

Metabolic dysregulation, encompassing conditions such as type 2 diabetes mellitus, obesity, metabolic syndrome, and dyslipidemia, poses an increasing global health burden. The dysregulation of 11β-hydroxysteroid dehydrogenase 1 (11β-HSD1), a key enzyme in glucocorticoid metabolism, has been strongly implicated in the pathogenesis of these disorders by influencing glucose homeostasis, lipid metabolism, and insulin sensitivity. Consequently, targeting 11β-HSD1 offers a promising therapeutic strategy for mitigating metabolic dysregulation and its associated complications.

The study aimed to identify resveratrol derivatives with high binding affinity and inhibitory potential against 11β-HSD1, using computational approaches to evaluate their pharmacokinetic and toxicity profiles.

A library of resveratrol derivatives was screened using molecular docking to identify high-affinity compounds. The hit compounds were further evaluated for absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, followed by molecular dynamics simulations to assess their stability.

The resveratrol cis-dehydrodimer emerged as the most promising candidate, demonstrating high binding affinity, favorable ADMET properties, and stability over a 200 ns simulation period. These findings suggest its potential as a small-molecule inhibitor of 11β-HSD1.

The resveratrol cis-dehydrodimer represents a viable candidate for further experimental validation as a therapeutic agent for metabolic disorders. Future studies should include synthetic validation and in vivo testing to confirm its efficacy.

Long COVID-19 (LC) is a condition that follows SARS-CoV-2, an acute infection defined by persistent fatigue, dyspnea, and impaired cognitive function. LC presents a complex array that imposes ongoing challenges on global health, patients' quality of life, and functional capacity. Many inconsistencies surround its pathophysiology, diagnosis, prevention, and treatment. This review aims to cover missed gaps in LC with a special focus on therapeutic strategies concerning non-pharmacological, pharmacological, experimental, and innovative approaches for better patient management and outcomes, as well as to evaluate their effectiveness and guide future research.

An online search was conducted using five digital repositories: PubMed, Scopus, Google Scholar, Web of Science, and the Cochrane Library. A combination of keywords associated with LC therapy was employed: “long COVID, “pharmacological options,” “non-pharmacological options,” “innovative strategies,” “experimental”, and” quality of life (QOL).” Relevant data were extracted and synthesized to categorize therapeutic approaches into subtypes. A critical analysis was conducted on their mechanism of action, indication, outcome, and limitations.

The pooled prevalence of LC was 42%, and the symptom duration ranged from 3 months to 2 years. The most important risk factors for LC were female sex, unvaccinated status, and cases with co-morbidities. Diagnosis of LC was challenging due to a lack of diagnostic standardization and reliable biomarkers.

Non-pharmacological strategies were employed first, showing diverse efficacies; however, the reported literature was hindered by small sampling. Pharmacological agents show promising results but need further validation. Experimental and innovative strategies need longer studies and validations.

LC has imposed a significant burden on community health, necessitating the appropriate allocation of health resources and community support. Preventive and therapeutic interventions show promise, but the variability in patient response underscores the need for personalized approaches and more well-designed trials. Collaborative research and multi-disciplinary teams are needed to mitigate the long-term effects of LC and improve patient outcomes.

Circadian rhythm genes (CRGs) play a significant role in the pathogenesis of various cancers, yet their impact on bladder cancer (BC) remains to be fully elucidated. EZH2, as a potential oncological biomarker, lacks clear delineation regarding its prognostic significance in BC. Furthermore, the effect of anesthesia on circulating tumor cells (CTCs) in cancer patients is scarcely studied.

In this study, we developed a bioinformatics signature based on CRGs to assess the prognosis of BC patients and investigated the expression of EZH2 in BC and its correlation with patient outcomes through clinical sample analysis. Furthermore, we collected blood samples from BC patients before anesthesia and two hours post-anesthesia, enriched for CTCs, and analyzed the expression of EZH2 to evaluate the impact of anesthesia on the quantity of CTCs and their EZH2 expression status.

Our prognostic model identified EZH2 as a key determinant of BC prognosis, with the high expression of EZH2 significantly associated with poor patient outcomes. Experimental validation revealed a significant increase in the number of EZH2⁺ CTCs after anesthesia in BC patients. These findings suggest that anesthesia may facilitate BC metastasis by increasing the number of EZH2⁺ CTCs.

The findings highlight the prognostic value of CRGs and EZH2 in BC, providing new insights into tumor biology and metastasis. Furthermore, this study suggests anesthesia may influence tumor progression by modulating EZH2 expression in CTCs, underscoring the need for careful anesthetic selection in BC patients.

This study unveils the potential value of CRGs and EZH2 in the prognostic assessment of BC and reports for the first time that anesthesia may influence tumor metastasis by modulating the expression of EZH2 in CTCs. These results offer new biomarkers for the prognosis and treatment of BC and provide novel insights into the role of anesthesia in cancer metastasis.

Neurodevelopmental disorders (NDDs) represent a diverse and heterogeneous group of conditions, including global developmental delay (GDD), autism spectrum disorder (ASD), and neurodevelopmental encephalopathy with epilepsy (NDEE). While these disorders often share phenotypic similarities, their underlying genetic causes can vary widely, making clinical diagnosis challenging.

In this study, we performed whole-genome sequencing (WGS) on a family having an autosomal recessive neurodevelopmental disorder. The proband (II-2) underwent WGS, followed by variant filtering through an in-house bioinformatics pipeline. Sanger sequencing and 3D protein modeling were performed to confirm the pathogenicity of the identified variant.

A novel biallelic missense variant in the NAV3 (c.3430T>C; p.Ser1144Pro) was detected using WGS and Sanger sequencing. Subsequently, 3D protein modeling revealed significant alterations in the secondary structure of NAV3, indicating a potential pathogenic effect.

The identification of a novel biallelic missense variant in NAV3 adds a new layer to our understanding of its potential contribution to autosomal recessive neurodevelopmental disorders. This case expands the mutational landscape of NAV3 and underscores its emerging significance in neurodevelopment.

This study reports a novel NAV3 variant in association with autosomal recessive NDD, contributing to the growing body of evidence supporting the involvement of NAV3 in human neurodevelopment. Functional validation and identification of additional patients will be essential to establish definitive genotype-phenotype correlations and uncover the mechanistic pathways underlying NAV3-associated disorders.

Ovarian reserve reflects the functional capacity of a woman’s ovaries, encompassing factors such as follicle quantity, egg quality, and fertilization potential. Assessment of ovarian reserve is essential in reproductive medicine, particularly for fertility evaluation and assisted reproductive technologies (ART). While traditional biochemical markers such as anti-Müllerian hormone (AMH) and follicle-stimulating hormone (FSH) are commonly used, instrumental diagnostic methods like ultrasound and magnetic resonance imaging (MRI) provide valuable morphological and functional insights. This systematic review without a comprehensive meta-analysis evaluates the role of ultrasound and MRI in assessing ovarian reserve and their potential applications in clinical and research settings.

A comprehensive literature search was conducted across multiple databases to identify relevant studies evaluating ovarian reserve using ultrasound and MRI. Studies were screened based on predefined inclusion criteria, focusing on imaging parameters such as ovarian volume, follicular count, stromal characteristics, and vascularization. The effectiveness of these imaging techniques was analyzed in comparison to established biochemical markers. Due to heterogeneity in the included studies, a systematic review was performed without a formal meta-analysis.

Ultrasound, particularly transvaginal ultrasound (TVUS), remains the gold standard for ovarian reserve assessment, allowing real-time visualization of antral follicle count (AFC), ovarian volume, and follicular morphology. Doppler ultrasound provides additional insights into ovarian blood flow, which correlates with follicular development and ovarian function. MRI offers high-resolution, three-dimensional imaging, enabling detailed assessment of ovarian structure, follicular density, and stromal composition. While MRI provides superior soft-tissue contrast, its role in routine ovarian reserve assessment is limited due to cost and accessibility. The findings indicate that although both modalities are valuable for ovarian reserve evaluation, there is no consensus on standardized imaging parameters for defining ovarian functional viability. The available literature also presents inconsistencies in the correlation between imaging findings and ovarian function.

Ultrasound and MRI are essential tools for assessing ovarian reserve, providing complementary morphological and functional data. However, the lack of standardized imaging parameters limits their ability to definitively determine ovarian functional viability. Further research is needed to establish validated diagnostic criteria and integrate imaging techniques with biochemical markers to enhance the accuracy of ovarian reserve assessment in clinical practice and reproductive research.

Early and precise diagnosis is essential for effectively treating and managing pulmonary tuberculosis. The purpose of this research is to leverage artificial intelligence (AI), specifically convolutional neural networks (CNNs), to expedite the diagnosis of tuberculosis (TB) using chest X-ray (CXR) images.

Mycobacterium tuberculosis, an aerobic bacterium, is the causative agent of TB. The disease remains a global health challenge, particularly in densely populated countries. Early detection via chest X-rays is crucial, but limited medical expertise hampers timely diagnosis.

This study explores the application of CNNs, a highly efficient method, for automated TB detection, especially in areas with limited medical expertise.

Previously trained models, specifically VGG-16, VGG-19, ResNet 50, and Inception v3, were used to validate the data. Effective feature extraction and classification in medical image analysis, especially in TB diagnosis, is facilitated by the distinct design and capabilities that each model offers. VGG-16 and VGG-19 are very good at identifying minute distinctions and hierarchical characteristics from CXR images; on the other hand, ResNet 50 avoids overfitting while retaining both low and high-level features. The inception v3 model is quite useful for examining various complex patterns in a CXR image with its capacity to extract multi-scale features.

Inception v3 outperformed other models, attaining 97.60% accuracy without pre-processing and 98.78% with pre-processing.

The proposed model shows promising results as a tool for improving TB diagnosis, and reducing the global impact of the disease, but further validation with larger and more diverse datasets is needed.

Cuproptosis, a newly discovered form of programmed cell death, has potential implications for tumorigenesis and cancer progression. This study investigates the role of cuproptosis in Acute Myeloid Leukemia (AML) and identifies associated biomarkers using bulk and single-cell RNA sequencing. Despite recent advances, the mechanisms of cuproptosis in AML remain unclear, and its relationship with immune cell infiltration could reveal novel therapeutic targets.

RNA-seq data from 151 AML patients and 70 healthy controls were obtained from TCGA and GTEx databases, and single-cell RNA-seq data from 16 AML patients (GEO) were used for validation. Differential expression of Cuproptosis-Related Genes (CRGs) was analyzed via RCircos and correlation analysis. Immune cell infiltration was assessed using CIBERSORT and ssGSEA. WGCNA identified key genes for AML and cuproptosis subtypes, which were validated with single-cell data. Intercellular communication was analyzed through ligand-receptor interactions. RNA interference experiments validated TLR4 and NCF2, with gene expression measured through RT-qPCR. Apoptosis and CCK-8 assays assessed cell viability.

We identified 19 CRGs with differential expression between AML subtypes linked to immune cell infiltration. Subtype analysis classified AML patients into C1 and C2 subgroups enriched in biosynthesis and metabolism pathways. WGCNA identified 2701 genes associated with AML and 92 with cuproptosis, leading to 15 intersecting genes. RETN was highlighted as key in intercellular communication. Experimental validation showed that elesclomol-induced cell death in THP-1 cells is reversible by TTM. Knockout of TLR4 and NCF2 promoted cuproptosis.

These findings offer new insights into the role of cuproptosis in AML, highlighting novel biomarkers, such as TLR4 and NCF2, which may provide promising targets for the development of future therapeutic strategies in AML treatment.

Doxorubicin (DOX) is a chemotherapeutic agent widely used for the treatment of various cancers; however, its clinical use is limited by its cardiotoxicity. However, the underlying molecular mechanisms remain poorly understood, hindering the development of effective preventive and treatment strategies. This study aimed to identify core target genes and explore the mechanisms involved in DOX-induced cardiotoxicity by integrating microarray analysis, machine learning, and molecular docking.

Differential expression analysis was performed using microarray data from DOX-induced cardiotoxic samples and healthy controls. Multiple machine learning algorithms were applied to identify core target genes. The predictive performance of these genes was evaluated using receiver operating characteristic (ROC) curves. Molecular docking was conducted to evaluate the binding affinity of DOX to the target genes. Functional analysis was performed to investigate potential toxic mechanisms.

In total, 276 differentially expressed genes were identified between DOX-induced cardiotoxicity samples and controls. The support vector machine algorithm demonstrated the best performance, leading to the identification of five core target genes: RAP1A, CTLA4, OR2M1P, TRIM53, and LOC149837. The ROC curves confirmed the strong predictive power of these genes, with area under the curve values greater than 0.85. Molecular docking showed stable binding between DOX and the target genes. Functional analysis suggested that the Rap1 signaling pathway and immune system regulation may be involved in DOX-induced cardiotoxicity.

Traditional toxicological studies often rely on limited experimental approaches that do not fully capture the complexity of disease mechanisms. The integration of microarray analysis, machine learning, and molecular docking in this study offers a comprehensive framework for investigating the toxicological pathways of DOX-induced cardiotoxicity, thereby providing insights into therapeutic development and safety regulations.

By combining microarray analysis, machine learning, and molecular docking, we identified five key target genes associated with DOX-induced cardiotoxicity. Functional analysis further suggested the involvement of the Rap1 signaling pathway and immune system regulation in DOX-induced cardiotoxicity. These findings offer insights into the molecular mechanisms underlying DOX-induced cardiotoxicity and have implications for the development of protective strategies and therapeutic interventions.

Endoplasmic reticulum stress (ER stress) plays a crucial role in influencing the malignant behaviors of various tumors. Targeting the expression or degradation of transcription factors (TFs) offers a promising avenue for cancer treatment. However, a detailed understanding of how ER stress affects TF function and their interactions remains limited. This study aims to develop a prognostic model and identify TFs associated with ER stress in pancreatic ductal adenocarcinoma (PDAC).

We obtained gene expression profiles and corresponding clinical data from The Cancer Genome Atlas (TCGA). To develop a prognostic signature, we performed several analyses, including unsupervised clustering, enrichment analysis, immune infiltration assessment, as well as univariate, LASSO, and multivariate Cox regression analyses. Four transcription factors—STAT1, IRF6, NRF1, and RXRA—were incorporated into a risk model, which was subsequently validated using the GSE dataset. Additionally, we examined IRF6 through quantitative PCR, western blotting, flow cytometry, and immunohistochemistry in vitro using pancreatic cancer cell lines and a tissue microarray.

The high-risk group identified by the model exhibited significant associations with immune cell infiltration and poorer survival outcomes, though there was no significant correlation with tumor purity (p = 0.19). Furthermore, IRF6 downregulation in vitro was found to inhibit pancreatic cancer cell proliferation and promote apoptosis. IRF6 depletion also increased the expression of key molecules involved in ER stress at both the transcriptional and translational levels. Immunohistochemical analysis revealed marked differences in IRF6 expression between tumor and adjacent non-tumor tissues (59.29±29.88 vs. 95.22±40.80, p<0.001).

This study provides evidence that the constructed risk model can effectively predict prognosis in PDAC patients. Transcription factors related to ER stress, such as IRF6, show promise as both prognostic biomarkers and potential therapeutic targets for PDAC.

Ciprofol is a novel sedative-anesthetic that functions similarly to propofol. This study aimed to evaluate the efficacy and safety of ciprofol for the induction and maintenance of general anesthesia in patients undergoing thoracoscopic surgery.

A total of 120 patients undergoing thoracoscopic surgery for pulmonary nodules under general anesthesia were randomly assigned to the ciprofol group or the propofol group. Patients in the ciprofol group received an initial dose of 0.4 mg.kg^-1 of ciprofol for anesthesia induction, followed by an infusion rate ranging from 0.4 mg.kg^-1.h^-1 to 2.4 mg.kg^-1.h^-1 for maintenance. In the propofol group, patients were administered an initial dose of 2.0 mg.kg^-1 of propofol for induction, with a maintenance infusion rate ranging from 4.0 mg.kg^-1.h^-1 to 12 mg.kg^-1h^-1. The primary outcome measured was the success rate of sedation. Secondary outcomes included the time to successful induction of anesthesia, changes in hemodynamics and bispectral index (BIS) within 10 minutes after the initial administration of the study medication, time to respiratory recovery and full alertness, and the incidence of adverse events.

The sedation success rate was 100% in both groups. In this study, statistical analyses revealed no significant differences in the time to eyelash reflex disappearance (p=0.599), induction success time (p=0.431), the moment when the BIS value first fell below 60 (p=0.538), the time to respiratory recovery (p=0.505), or the interval until full wakefulness (p=0.837). Notably, within the first 10 minutes following the initial administration of the study medication, the reduction in blood pressure was significantly more pronounced in the propofol group (p<0.05). Additionally, the mean BIS value was significantly higher in the propofol group (p<0.01). The required dosage of sedative medication was significantly lower in the ciprofol group (p<0.001). Compared to the propofol group, the ciprofol group exhibited a significant reduction in the incidence of adverse events during intubation (p=0.01), a marked decrease in injection pain (p=0.001), and a significant decrease in the incidence of intraoperative hypotension (p<0.05).

Ciprofol exhibits comparable efficacy and safety profiles for both the induction and maintenance of general anesthesia in patients undergoing thoracoscopic surgery. Furthermore, it has been associated with a reduced dosage requirement, significantly lower mean BIS values, and a notable decrease in the incidence of injection pain and intraoperative hypotension.

(ChiCTR2400086976).

Acute myeloid leukemia (AML) is the most common adult hematologic malignancy, with relapse and drug resistance posing major challenges despite treatment advances. Cryptotanshinone (CTS), a diterpenoid compound derived from Salvia miltiorrhiza, exhibits anticancer activity in various tumors. However, its role and mechanisms in AML remain unclear. This study aims to investigate the inhibitory effects of CTS on AML cells and its potential mechanisms.

Network pharmacology was employed to identify potential AML-related targets of CTS, and a disease-drug-target interaction network was constructed. The effects of CTS on KG-1 cells were assessed using CCK-8 proliferation assays, cell cycle analysis and apoptosis detection. Western blot and quantitative real-time polymerase chain reaction (qRT-PCR) were performed to analyze the regulatory effects of CTS on the endoplasmic reticulum stress (ERS) signaling pathway. The role of the Hippo-YAP signaling pathway in CTS-induced AML inhibition was further explored.

Network pharmacology analysis identified key AML-related targets of CTS, enriched in multiple cancer-related signaling pathways. Experimental results showed that CTS inhibited KG-1 cell proliferation in a dose-dependent manner, induced S-phase arrest, and promoted apoptosis. Furthermore, CTS treatment significantly upregulated ERS-related key proteins. While YAP overexpression attenuated CTS-induced ERS activation and reduced apoptosis levels.

This study indicates that CTS inhibits AML cell proliferation and induces apoptosis while activating the ERS signaling pathway. However, aberrant activation of the Hippo-YAP pathway weakens this effect. These findings provide novel theoretical insights into potential therapeutic strategies for AML.

Pterygium is a common ocular surface disorder characterized by fibrovascular overgrowth, with recurrence remaining a major clinical challenge. While non-coding RNAs have been implicated in pterygium pathogenesis, the role of tRNA-derived small RNAs (tsRNAs) remains unexplored.

We performed small RNA sequencing on pterygium and adjacent normal conjunctiva tissues to profile tsRNA expression. Differentially expressed tsRNAs were validated using qRT-PCR, and their biological functions were investigated via cell proliferation and wound healing assays in human pterygium fibroblasts (HPF). Potential target genes and enriched pathways were analyzed using bioinformatics approaches, including KEGG and GO enrichment analysis.

We identified significantly dysregulated tsRNAs in pterygium, with tRF-1_30-His- GTG-1, tRF-1_31-Val-CAC-2, tRF-1_31-Gly-GCC-1, and tRF-1_30-Gly-CCC-1-M4 exhibiting notable upregulation. Functional assays demonstrated that tRF-1_30-His- GTG-1 promotes fibroblast proliferation and migration, while the other three tsRNAs enhance fibroblast migration. Pathway enrichment analysis revealed their involvement in cellular proliferation, extracellular matrix remodeling, and angiogenesis.

This study provides the first evidence of tsRNA involvement in pterygium pathogenesis, highlighting their potential as biomarkers and therapeutic targets. Future studies should focus on deciphering their precise regulatory mechanisms and developing RNA-based therapeutic strategies to mitigate disease progression.

With the rapid emergence of drug-resistant strains of tuberculosis, resistance to current first-line and second-line anti-tuberculosis drugs is becoming increasingly prevalent. Consequently, the discovery of new lead compounds is essential to address this challenge. GyrB has emerged as a promising target for tuberculosis treatment due to its pivotal role in DNA replication and topology regulation in Mycobacterium tuberculosis.

In this study, a multi-conformational virtual screening approach, complemented by antibacterial activity assays, was utilized to identify novel GyrB inhibitors from the ChemDiv database.

Among the 27 compounds purchased, 10 exhibited significant inhibitory effects against the H37Rv strain, with 8 featuring novel core scaffolds. Notably, three compounds (V027-7669, V017-8710, and 5132-0213) demonstrated a minimum inhibitory concentration (MIC) of 8 μg/mL. Compounds V027-7669 and V017-8710, in particular, showed antibacterial activity against a multidrug-resistant tuberculosis strain, with MIC values of 32 μg/mL and 16 μg/mL, respectively. Molecular dynamics simulations revealed that both V027-7669 and V017-8710 bind stably to GyrB, which are primarily driven by nonpolar interactions. Furthermore, both of them occupy a novel sub-pocket formed by residues Val99, Gly106, Val123, Gly124, and Val125, where they establish hydrogen bonds with Val125.

Our study underscores the effectiveness of a multi-conformational virtual screening strategy in identifying novel GyrB inhibitors and suggests V027-7669 and V017-8710 as promising lead compounds for the development of treatments against multidrug-resistant tuberculosis.

Diabetes mellitus (DM) is a chronic metabolic disorder that seeks treatment instead of available mitigative therapy.

Six (E)-3-(aryl)-1-phenylprop-2-en-1-one chalcones were synthesized and characterized through various spectroscopic techniques. Their anti-diabetic potential was examined through in-vitro (α-glucosidase and α-amylase inhibition assays), in-vivo (alloxan-induced hyperglycemia), and in-silico studies.

All the chalcones derivatives significantly inhibited α-glucosidase and α-amylase. Compounds 11 (IC50 = 1.10 ± 0.02) and 13 (IC50 = 3.25 ± 0.10 µM) exhibited the most potent activity against α-glucosidase. The effect of compounds 11 and 13 was also significant against α-amylase with IC50 of 13.2 ± 0.50 and 10.2 ± 0.4 µM, respectively. In alloxan-induced hyperglycemic model, a significant (p<0.001) reduction in blood glucose level (BGL) was observed by compounds 10, 11 and 14 with maximum percent inhibition of 47.48, 47.22 and 47.55, respectively. In the oral glucose tolerance test, a continuous reduction in BGL was noted at 60 minutes. No negative effect was seen on lipid profile, and in liver and renal function tests. However, a slight gain in body weight was noted. Moreover, docking result indicates good interaction of these molecules with the target enzymes, α-glucosidase and α-amylase.

These results demonstrate that all these molecules have significant anti-diabetic potential.

Alzheimer's disease (AD) is the most common cause of dementia worldwide, with a steadily increasing prevalence. However, the mechanisms underlying AD remain unclear, and current treatments have only limited efficacy.

This study aimed to identify potential biomarker genes for AD and to explore the underlying mechanisms by integrating microarray analysis, Mendelian randomization (MR), and experimental validation.

AD-related microarray datasets were downloaded from the Gene Expression Omnibus database. Differential expression analysis identified differentially expressed genes (DEGs) between AD and control samples. Summary-level data from genome-wide association studies on AD were integrated with expression quantitative trait loci data to identify genes with potential causal relationships with AD using MR. The intersections between DEGs and causal genes were identified as hub genes. Functional analysis was performed to explore underlying mechanisms. Quantitative real-time PCR was applied to validate the expression of hub genes in clinical samples.

Differential expression analysis identified 312 DEGs, whereas MR identified 202 genes with causal effects on AD. The intersection of these two sets identified four hub genes: FCRLB, MT2A, PFKFB3, and SRGN. Functional analysis indicated significant associations between AD and immune-related pathways. Correlation analysis revealed significant connections between hub genes and immune cells in AD. The expression of MT2A, PFKFB3, and SRGN was significantly upregulated, whereas FCRLB was downregulated in clinical AD samples compared with controls.

The integration of microarray analysis, MR, and experimental validation identified and validated four potential biomarker genes with causal effects on AD, namely FCRLB, MT2A, PFKFB3, and SRGN. Functional analysis indicated a pivotal role of the immune microenvironment in AD. These findings offer insights into the molecular mechanisms of AD and have implications for improving its diagnosis and treatment strategies.

RNA sequencing data and clinical information from GC patients were collected from the TCGA and GEO databases, excluding cases of pure IGC and DGC. Based on ECMR and CA-related genes, supervised clustering via non-negative Matrix Factorization (NMF) was used to identify molecular subtypes in MGC. Differential expression and Cox regression analyses were performed to identify prognostic genes, and an ECMR and CA-based gene signature (ECRS) was developed using machine learning techniques. Gene Set Variation Analysis (GSVA) was conducted to assess functional differences between risk groups, while TIDE and pRRophetic analyses were used to predict responses to immunotherapy and chemotherapy.

A total of 239 MGC patients were classified into two molecular subtypes with significant differences in prognosis. Subtype 2 displayed distinct ECM interactions and connective tissue development pathways. To refine the ECRS model, we tested 117 model combinations across 10 machine learning algorithms, selecting the configuration with the best predictive accuracy. This optimized model distinguished biological and immune characteristics between high- and low-risk groups, with low-risk patients showing greater sensitivity to immunotherapy and standard chemotherapy.

This study identifies novel molecular subtypes of MGC based on ECMR and CA-related genes and establishes an effective ECRS model to predict prognosis, immunotherapy response, and chemotherapy sensitivity. This model supports personalized treatment strategies for MGC.

Hepatocellular carcinoma (HCC) is a life-threatening cancer with rising incidence and mortality rates. Identifying new prognostic biomarkers is crucial for improving HCC management.

This study investigates the role of Double PHD Fingers 1 (DPF1) in hepatocellular carcinoma (HCC), exploring its potential as a prognostic indicator and therapeutic target.

We analyzed DPF1 expression in 374 hepatocellular carcinoma (HCC) tissues and 50 normal tissues from the TCGA-HCC database, as well as in 240 HCC tissues and 202 normal tissues from the ICGC-HCC repository. We examined the correlation between DPF1 expression and clinical parameters, immune cell infiltration, drug response profiles, cancer stem cell (CSC) characteristics, and its diagnostic/prognostic potential using various bioinformatics tools and statistical analyses. Validation was performed using the ICGC and HPA databases, and qRT-PCR was used to confirm DPF1 expression in HCC cell lines.

DPF1 exhibited abnormal expression in HCC and several other malignancies. Elevated DPF1 levels were significantly associated with higher Alpha-fetoprotein (AFP) levels (p = 0.043) and poorer clinical outcomes, including diminished overall survival (OS) (p = 0.002), progression-free survival (PFS) (p = 0.018), and disease-specific survival (DSS) (p = 0.001). DPF1 expression was also linked to immune cell infiltration, immune checkpoint gene expression, drug sensitivity, and CSC characteristics. Notably, DPF1 was significantly overexpressed in HCC tissues and cell lines at both transcriptional and translational levels.

Our study reveals that DPF1 is a novel prognostic biomarker in HCC, with potential implications for immunotherapy and drug resistance. Elevated DPF1 expression is associated with adverse clinical outcomes and may serve as a target for future therapeutic interventions in HCC.

Hepatocellular carcinoma (HCC) has a poor prognosis due to late diagnosis and rapid progression, highlighting the need for a deeper understanding of its pathogenesis. Lysine crotonylation (Kcr), a unique post-translational modification, plays a crucial role in epigenetic regulation. However, the role of crotonylation-related genes (CRGs) in HCC remains poorly understood, necessitating an investigation of their prognostic and therapeutic relevance.

Transcriptomic and clinical data were obtained from TCGA and GEO databases. A CRG-based risk score was developed using Cox and LASSO regression analyses. To enhance survival prediction, a nomogram incorporating the risk score was constructed. Immune cell infiltration and drug sensitivity were assessed using CIBERSORT and 'OncoPredict.' Single-cell sequencing was employed to examine CRG expression within the HCC tumor microenvironment.

An 8-gene risk score model (HDAC2, ACADS, HDAC1, ENO1, PPARG, ACADL, ACSL6, and AGPAT5) was established, effectively stratifying patients into high- and low-risk groups in the training set. Cox regression and Kaplan-Meier analyses validated its prognostic value in the test set. The nomogram demonstrated enhanced prognostic accuracy for survival prediction. Differences in immune cell infiltration and immune checkpoint expression between risk groups highlighted the association between CRGs and the tumor immune microenvironment. Single-cell sequencing revealed that CRGs were highly expressed in key immune cells within the HCC microenvironment. Additionally, drug sensitivity analysis suggested that specific targeted therapies may be more effective in HCC patients.

Crotonylation-related gene signature demonstrates strong prognostic value in hepatocellular carcinoma (HCC), effectively stratifying patients into high- and low-risk groups and recapitulating known oncogenic roles of HDAC1/2, ENO1, PPARG, AGPAT5 and the protective functions of ACADS, ACADL, and ACSL6. It was found that crotonylation not only influences tumor cell metabolism and epigenetic regulation but also shapes the immune microenvironment, highlighted by distinct checkpoint expression, differential immune cell infiltration, and drug sensitivity profiles, which position our model as a promising tool for personalized therapeutic decision-making. However, clinical translation will require standardized, reproducible assays for crotonylation measurement and rigorous validation across diverse HCC etiologies (e.g., viral vs. non-viral), along with mechanistic and longitudinal studies to dissect causality versus correlation, assess off- target effects of crotonylation modulators, and confirm functional impacts on immune modulation before routine diagnostic or therapeutic use.

This study identifies a prognostic CRG signature for HCC and provides novel insights into personalized treatment strategies.

The incidence of psychiatric disorders, such as post-traumatic stress disorder (PTSD), anxiety, and depression, has been steadily increasing, while current treatment approaches remain limited in efficacy. As a result, there is an urgent need to explore more effective therapeutic interventions. In recent years, MDMA (3,4-methylenedioxymethamphetamine)-assisted therapy (MDMA-AT) has emerged as a promising and innovative approach, demonstrating favorable clinical potential in the treatment of these disorders. Although preliminary studies have confirmed its therapeutic efficacy, a comprehensive and systematic analysis of the research trends and current limitations of MDMA-AT remains lacking.

This study employed a bibliometric approach to systematically retrieve and analyze research literature published between 1994 and 2023 on the application of MDMA in the treatment of PTSD, anxiety, and depression. Relevant data were obtained from three prominent databases: Web of Science Core Collection, PubMed, and Scopus. VOSviewer and Microsoft Excel were used to perform visual and quantitative analyses, focusing on publication trends, research hotspots, prolific authors, leading institutions, and international collaboration networks.

The findings indicated a substantial increase in MDMA-related research over the past decade. The United States has led the field in publication output, with the Multidisciplinary Association for Psychedelic Studies (MAPS) identified as the most productive institution. Key figures, such as Rick Doblin, have demonstrated high influence and centrality within the global research network. The research focus has gradually shifted from investigations of the neurotoxic properties of MDMA to explorations of its therapeutic mechanisms, safety profiles, and clinical applications.

This study provides a comprehensive synthesis of the past thirty years of research on MDMA-AT in the treatment of PTSD, anxiety, and depression, identifying major research trajectories and critical challenges in the field. While current findings highlight the therapeutic promise of MDMA and its translational potential, further research is needed to improve trial design, enhance sample diversity, and evaluate long-term effects in order to support the standardization and evidence-based integration of MDMA-assisted therapy into clinical practice.

Anophthalmia/microphthalmia (A/M) and anterior segment dysgenesis (ASD) are severe ocular anomalies impacting eye morphology, occurring in 30 per 100,000 live births. Genetic research has identified over 30 genes linked to A/M anomalies, with their products mainly involved in eye organogenesis.

This study examined two consanguineous A/M families to identify disease-associated pathogenic mutations and predict their functional impact.

Patients were clinically examined using A-scan and ophthalmic ultrasonography. Whole exome sequencing (WES) identified candidate pathogenic variants validated through Sanger sequencing. Computational analyses assessed the impact of these mutations on protein structure and function.

The clinical diagnosis of family A revealed microphthalmia with ASD, while family B presented with an A/M phenotype. Exome analysis of family A identified a novel missense variant, NM_012293:c.A3742G [p.(Arg1248Gly)], in the peroxidasin (PXDN) gene (ClinVar ID: VCV001333267.1). At the cellular level, PXDN is involved in establishing sulfilimine bonds in collagen IV, a component of the basement membrane, suggesting that ocular defects may result from impaired integrity of the basement membrane in the developing eye. In contrast, Family B exhibited a nonsense variant NM _012186:c.720C>A (p.Cys240*) in the FOXE3 gene. This variant has been previously reported in other South Asian populations, suggesting a founder effect in subcontinent populations. Structural modeling and simulation analysis of mutant proteins revealed altered properties, thus corroborating the pathogenicity of the identified mutation.

Our findings may contribute to the elucidation of genotype-phenotype correlations, potentially facilitating the molecular diagnosis of microphthalmia and ASD.

Peripheral T-cell lymphoma (PTCL) is a rare and heterogeneous group of hematological malignancies. Treatment options are limited and often unsatisfactory, leading to a poor prognosis in most subtypes.

This study aimed to identify potential biomarker genes for PTCL and to explore the underlying mechanisms by integrating machine learning, Mendelian Randomization (MR), and experimental validation.

Microarray datasets (GSE6338, GSE14879, and GSE59307) were downloaded from the Gene Expression Omnibus database. Differential expression analysis was conducted to identify the Differentially Expressed Genes (DEGs) between patients with PTCL and controls. A machine learning algorithm was then used to further refine the selection of characteristic genes for PTCL. We integrated genome-wide association studies data with expression quantitative trait loci data to identify genes with potential causal relationships to PTCL. Functional analysis was performed to explore underlying mechanisms. Finally, the identified gene was validated in clinical samples from patients with PTCL and controls.

Based on 60 DEGs, the least absolute shrinkage and selection operator algorithm identified nine characteristic genes for PTCL. MR analysis revealed 203 genes with causal effects on PTCL, ultimately identifying one co-expressed gene: Basic Leucine Zipper ATF-like Transcription Factor 3 (BATF3). It demonstrated good predictive performance across various PTCL subtypes, with AUC values ranging from 0.7 to 1. Functional analysis suggested that BATF3 may play a role in PTCL through immune-related pathways. Experimental validation using clinical samples further suggested the potential of this biomarker gene in PTCL.

By combining machine learning, MR, and experimental validation, we identified and validated BATF3 as a promising biomarker of PTCL. These findings provide insights into the molecular mechanisms underlying PTCL and may inform the development of effective treatment strategies for this disease.

Metastasis is the leading cause of death in lung cancer patients. Pre-metastatic niche (PMN) plays an important role in pre-metastatic tumors. However, the development of clinical applications of PMN is still limited.

Expression data for lung adenocarcinoma (LUAD) patients and PMN-related genes were downloaded from the UCSC Xena website and GeneCards database, respectively. Multiple combinations based on machine learning algorithms were used to screen signature genes and construct a PMN-associated index. Spearman analysis explored the correlation between the PMN-associated index and immune cell infiltration. In addition, we analyzed the clinical value of the PMN-associated index based on drug sensitivity analysis and TIDE scores.

The enrichment analyses suggested that PMN-related genes were mainly enriched in the PI3K-Akt and HIF-1 signaling pathways. We chose random survival forest, Lasso, and multivariate Cox regression analyses to construct the PMN-associated index based on the results of multiple machine learning algorithms. Six signature genes (SNAI2, CXCR4, TNFSF11, ENG, TIMP1, and PDGFB) were screened to construct the PMN-associated index. KM analysis suggested that the survival probability was greater in the low PMN-associated index group than in the high PMN-associated index group. In addition, we confirmed that LUAD patients with a low PMN-associated index were more likely to benefit from immunotherapy.

We confirmed that the PMN-associated index is a valid predictor of prognosis, immune characteristics, and antitumor therapy efficacy in LUAD patients, which provides additional evidence for the potential clinical value of PMN development.

Sepsis remains a leading cause of global morbidity and mortality.

To identify candidate biomarkers that may be mechanistically related to the pathogenesis of sepsis.

The Gene Expression Omnibus database was leveraged to identify differentially expressed genes (DEGs) between the healthy control and septicemia groups. Genes causally related to sepsis were probed through the integration of GWAS and expression quantitative trait loci (eQTL) data in a two-sample Mendelian randomization (MR) analysis. A set of key sepsis-related genes was then selected based on the overlap between these putative causal genes and the DEGs. These genes were then subjected to enrichment analyses, testing set validation, and analyses of their expression dynamics in clinical samples.

An examination of the overlap between 228 sepsis-related DEGs identified in the training dataset and 275 candidate causal genes linked to sepsis derived from the MR analysis led to the selection of four overlapping (SLC22A15, IL5RA, HDC, and SLC46A2) that may play a key role in sepsis. Enrichment analyses indicated that these genes were involved in the regulation of histidine metabolism and immune/inflammatory responses. In immune cell infiltration analyses, these genes were positively correlated with inflammatory response activation and the suppression of adaptive immunity. Consistent findings were obtained through qPCR verification in clinical samples.

These results offer potential insight into the mechanisms that govern septicemia and thus suggest a promising series of candidates that may be amenable to targeting to prevent or treat sepsis more effectively.

The emergence of the monkeypox virus (MPXV) as a zoonotic threat has necessitated the development of effective treatments, particularly after it spread to regions outside of Central and Western Africa, such as the 2003 outbreak in the United States. Our groundbreaking study identifies CDK1 and TOP2A as key proteins in the pathogenesis of MPXV infection, utilizing network pharmacology to target these proteins for the first time. CDK1 and TOP2A, previously known for their roles in cell reprogramming, emerge as critical targets in our strategy to combat the virus.

By targeting CDK1 and TOP2A, proteins integral to cell reprogramming, with small molecules identified in our study, such as carnosic acid, rosmarinic acid, and coclaurine, we propose a novel method not only to inhibit the replication of the monkeypox virus but also to harness cellular plasticity for therapeutic purposes. The identification and targeting of these proteins with specific compounds disrupt the virus's life cycle and simultaneously enhance the efficiency of cell reprogramming.

This dual-action approach leverages the inherent plasticity of cellular reprogramming processes to combat the virus, showcasing a pioneering step in the use of regenerative medicine principles for antiviral strategies. Moreover, molecular docking and dynamic simulations strengthen our findings by demonstrating a strong binding affinity between TOP2A and CDK1, validating the synergistic effects of our identified small molecules.

Our research thus opens new avenues for addressing viral threats like monkeypox, utilizing the convergence of virology, network pharmacology, and cellular reprogramming to pave the way for innovative treatments.

Type 2 diabetes (T2D) is a disease of high prevalence that is expected to continue increasing despite the pharmacological treatments available; in most cases, it is difficult to control. Therefore, more research on experimental drugs is necessary to propose better treatments.

This study aimed to identify the molecular alterations of pancreatic islets in type 2 diabetes through multi-omics data integration and possible pharmacological targets using bioinformatics methods.

In this study, the OmicsNet tool was used to integrate the multi-omics data associated with T2D, and the protein-protein interaction was visualized. Then, gene ontology and KEGG pathways analyses were carried out. Using the DrugRep server, the hub genes obtained underwent a virtual screening with experimental drugs, and twelve experimental drugs were selected to execute the molecular docking by CB-Dock2. Finally, the interactions were displayed in BIOVIA software.

Our results showed that the main molecular alterations of pancreatic islets in T2D were enzyme binding, mitochondrial metabolism, transcription factors, etc. They were involved in glucose uptake, receptor insulin signaling, and secretion. The molecular docking showed that SRC, AKT1, CREBBP, and HSP90AA1 were therapeutic targets for DB02729, DB04877, DB07970, DB07789, and DB03373.

We identified some alterations in the pancreas of patients with T2D, ten hub genes, and five experimental drugs that could potentially correct gene expression abnormalities. However, further studies are required to validate these results.

Ovarian cancer (OV) is one of the deadliest gynecologic cancers, and approximately 75% of serous ovarian cancer (SOC) patients are diagnosed at advanced stages due to the lack of effective biomarkers.

Immunogenic cell death (ICD) has been investigated in many comprehensive studies, and the role of ICD in ovarian cancer and its impact on immunotherapy is not yet known.

The NMF clustering analysis was employed to categorize OV samples into different subgroups. Survival, mutation, and CNV analyses were performed in these clusters. ESTIMATE, CIBERSORT, TIDE, and drug sensitivity analyses (based on GDSC) were also performed on the subtypes. Then, differentially expressed immunogenic cell death genes (DE-ICDGs) in OV were obtained by crossing the DEGs between cluster3 vs. cluster1, DEGs from the TCGA-GTEx dataset, and DEGs from the GSE40595 dataset. Functional enrichment analysis of DE-ICDGs was then performed. The signature genes related to the prognosis of OV in three OV datasets were excavated by drawing Kaplan-Meier curves. Finally, quantitative real-time PCR (qRT-PCR) was performed to verify the expression trends of the signature genes.

The NMF clustering analysis categorized OV samples into three distinct groups according to the expression levels of ICDGs, with differential analysis indicating that Cluster3 represented the subgroup with high ICD expression. Mutation and CNV analysis did not differ significantly between clusters, but Amp and Del's numbers did. Immuno-infiltration analysis revealed that cluster3 showed significant differences from cluster1 and cluster2. Immunotherapy and drug sensitivity analysis showed differences in immunotherapy and chemotherapy sensitivity between the clusters. The DEGs in cluster3 vs. cluster1, TCGA-GTEx dataset and GSE40595 dataset were intersected to obtain a total of 71 DE-ICDGs, and functional enrichment result suggested that the DE-ICDGs were significantly correlated with inflammatory response, complement system and positive regulation of cytokine production. 2 DE-ICDGs (FN1 and LUM) were identified that were associated with OV prognosis and were validated significantly down-regulated in the SOC group with PCR.

We identified ICD-associated subtypes of OV and mined 2 OV prognostic genes (FN1 and LUM) associated with ICD, which may have important implications for OV prognosis and therapy.

Ferula assa-foetida L. has traditionally been used to treat various diseases, including infections, asthma, stomach aches, and flatulence. Previous studies have highlighted its anti-inflammatory, anti-oxidative, anti-diabetic, neuroprotective, and nerve-stimulating properties.

This study aimed to evaluate the therapeutic effects and molecular mechanisms of action of the oleo-gum resin from Ferula assa-foetida L. in an animal model of diabetic neuropathy (DN).

The essential oil of oleo-gum resin from Ferula assa-foetida L. was analyzed using Gas Chromatography-Mass Spectrometric Analysis. Forty-two male Wistar rats were included in the study, with diabetes induced via streptozotocin (STZ) injection. The rats were randomly assigned to seven groups (n=6 per group) and treated with different doses of Ferula assa-foetida L. extract (100, 200 mg/kg/day) or oil (10, 20 mg/kg/day), alongside appropriate control groups. After a five-week treatment period, samples of dorsal root ganglia (DRG), pancreatic tissue, and blood were collected. Key parameters assessed included blood glucose and insulin levels, motor function tests, oxidative stress protein generation, pro-inflammatory cytokine gene expression, and histopathological analyses.

Treatment with various doses of Ferula assa-foetida L. extract or oil, as well as gabapentin, led to significant improvements. These included reduced blood sugar levels, increased insulin levels, and improved glycemic control. Motor function was enhanced, while the expression of pro-inflammatory cytokines and oxidative stress markers was significantly reduced.

These findings indicate a promising therapeutic approach for managing DN. Further studies are warranted to elucidate the underlying mechanisms of Ferula assa-foetida L.'s beneficial effects in DN.

This study aimed to construct a diagnostic risk model for Bipolar Disorder (BD) using inflammation-related genes (IRGs) and to explore the role of immune cell infiltration in BD pathogenesis.

BD datasets (GSE23848, GSE124326, GSE39653, and GSE46449) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the edgeR package. The intersection of DEGs and IRGs was defined as differentially expressed IRGs. A LASSO regression model was used to identify optimal biomarkers, which were then utilized to construct a diagnostic risk model. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic accuracy of the biomarkers. Internal validation was performed with GSE124326, while external validation utilized GSE23848, GSE39653, and GSE46449. The xCell module in the IOBR package was employed to assess immune cell infiltration proportions. The relationship between IRGs, the diagnostic risk model, and immune cell dynamics was further analyzed.

A total of 2345 DEGs were identified in GSE124326. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses indicated that inflammatory pathways are critically involved in BD pathogenesis. A total of 69 BD-related IRGs were identified. Six key IRGs (IL33, DNASE1L3, IL2RA, CD70, CLEC5A, and SLPI) were identified through LASSO regression analysis and used to develop a diagnostic risk model. Internal and external validations confirmed the robust diagnostic performance of the risk model. Immuno-infiltration analysis showed significant differences in immune cell infiltration between BD patients and healthy controls. The diagnostic risk model and four potential biomarkers (DNASE1L3, IL2RA, CD70, and SLPI) showed strong correlations with various immune cell types.

A diagnostic risk model for BD was constructed based on IRGs, highlighting the critical role of immune cell infiltration in BD pathogenesis.

The precise function of DDX59 Antisense RNA 1 (DDX59-AS1) in lung adenocarcinoma (LUAD) has yet to be fully elucidated.

This study uses bioinformatics analysis and experimental validation to investigate the association between DDX59-AS1 and LUAD.

This study uses statistical analysis and database interrogation to investigate the potential association between DDX59-AS1 expression and various clinical characteristics, prognostic factors, regulatory networks, and immune infiltration in LUAD. The quantification of DDX59-AS1 expression in LUAD cell lines is conducted through the use of quantitative real-time polymerase chain reaction (qRT-PCR).

DDX59-AS1 showed significantly elevated levels of expression in patients with LUAD. High levels of DDX59-AS1 expression were found to be significantly associated with poorer overall survival (OS) in patients with LUAD (p = 0.024). Furthermore, an independent correlation was observed between high DDX59-AS1 expression (p = 0.037) and OS in LUAD patients. DDX59-AS1 was found to be involved in various pathways, including glutathione metabolism, proteasome function, and the cytosolic DNA sensing pathway, among others. A significant correlation was observed between the expression levels of DDX59-AS1 and immune cell infiltration in the context of LUAD. Notably, elevated expression of DDX59-AS1 was observed in LUAD cell lines compared to the non-cancerous Beas-2B cell line.

A significant correlation was observed between elevated DDX59-AS1 expression in patients with LUAD and adverse prognosis, alongside increased immune infiltration. These results indicate that DDX59-AS1 may function as a prognostic marker for LUAD and a potential predictor of immunotherapy response.

A series of benzylidene derivatives of fenobam and its thio analogues (1-22) have been evaluated for their cytotoxicity against breast cancer (MCF-7, MDA-MB-231), ovarian cancer (A2780, SKOV-3) and cervical cancer (HELA) cell lines.

These compounds (1-22) exhibited 72-83% inhibition of Erk activity against the ovarian cancer cell line (A2780). Compounds 3 and 20 showed the highest DNA damage effect in Comet Assay against the A2780 cancer cell line as compared to the other tested analogues (4, 8, 11, 12, and 13) by using % Tail DNA and OTM. Compounds 3, 4, and 11 showed significant activities and selectivity towards COX-2 with 78%, 97%, and 89% inhibition, as compared to 17%, 57%, and 26% inhibition against COX-1 isoenzyme, respectively.

Interestingly, molecular docking scores were also in very good agreement with the experimental results regarding discriminating the selectivity index of the tested compounds against COX-1 & COX-2 enzymes. Further molecular dynamics (MD) simulation study revealed that the most selective compound, 13, binds with the COX-2 enzyme in a similar fashion to that of Rofecoxib, which was further supported by their MD-based free binding energies (MM-GBSA) of -49.76 ± 4.27 kcal/mol, and -44.84 ±3.78 kcal/mol, respectively.

Moreover, in silico ADMET predictions showed adequate properties for these compounds, making them promising leads worthy of further optimization.