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Background: The Csangos are an East-Central European ethnographic group living mainly in east of Transylvania in Romania. Traditionally, ethnography distinguishes three Csango subpopulations, the Moldavian, Gyimes and Burzenland Csangos. In our previous study we found that the Moldavian Csangos have East Asian/Siberian Turkic ancestry components that might be unique in the East-Central European region and might help to better understand the history of Hungarian speaking ethnic groups of the area. Since then, we obtained further Csango samples from Moldavia and from a distinct region of Gyimes, which two Csango subgroups are traditionally different since they live in a degree of isolation not only from other people but also from each other. Here we present the first genomic analysis of Gyimes Csangos, which intended to compare the genetic makeup of these two Csango subgroups using both allele-frequency and haplotype-based methods. The main goal of the study was to investigate the genetic isolation of the Csangos on a genome-wide SNP basis and to assess the isolation of Gyimes Csangos, which in contrast to the Moldavians was not yet studied. Results: Our results show that these two Csango groups show slight differences from each other. We confirmed the genetic isolation of Moldavian Csangos and revealed that Gyimes Csangos have a similar, but detectably weaker isolation. In the case of Gyimes Csangos we detected also a stronger East European or presumably Asian derived ancestry. Conclusion: The Gyimes Csangos show a degree of genetic isolation comparable to that of the Moldavians. The Asian ancestry that differentiates the Moldavian Csango people from the other East-Central European populations may be present in the Gyimes Csangos in an even higher degree, since Gyimes Csango individuals show a more significant share from that ancestry component. [ABSTRACT FROM AUTHOR]

Leukemia is a form of cancer that affects the bone marrow and lymphatic system, and it requires complex treatment strategies that vary with each subtype. Due to the subtle morphological differences among these types, monitoring gene expressions is crucial for accurate classification. Manual or pathological testing can be time-consuming and expensive. Therefore, data-driven methods and machine learning algorithms offer an efficient alternative for leukemia classification. This study introduced a novel super learning model that leverages heterogeneous machine learning models to analyze gene expression data and classify leukemia cells. The proposed approach incorporates an entropy-based feature importance technique to identify the gene profiles most significant to the labeling process. The strength of this super learning model lies in its final super learner, Random Forest, which effectively classifies cross-validated data from the candidate learners. Validation on a gene expression monitoring dataset demonstrates that this model outperforms other state-of-the-art models in predictive accuracy. The study contributes to the knowledge regarding the use of advanced machine learning techniques to improve the accuracy and reliability of leukemia classification using gene expression data, addressing the challenges of traditional methods that rely on clinical features and morphological examination. [ABSTRACT FROM AUTHOR]

DNA data storage has emerged as a solution for storing massive volumes of data by utilizing nucleic acids as a digital information medium. DNA offers exceptionally high storage density, long durability, and low maintenance costs compared to conventional storage media such as flash memory and hard disk drives. DNA data storage consists of the following steps: encoding, DNA synthesis (i.e., writing), preservation, retrieval, DNA sequencing (i.e., reading), and decoding. Out of these steps, DNA synthesis presents a bottleneck due to imperfect coupling efficiency, low throughput, and excessive use of organic solvents. Overcoming these challenges is essential to establish DNA as a viable data storage medium. In this review, we provide the overall process of DNA data storage, presenting the recent progress of each step. Next, we examine a detailed overview of DNA synthesis methods with an emphasis on their limitations. Lastly, we discuss the efforts to overcome the constraints of each method and their prospects. [ABSTRACT FROM AUTHOR]

Abstract Background The Csangos are an East-Central European ethnographic group living mainly in east of Transylvania in Romania. Traditionally, ethnography distinguishes three Csango subpopulations, the Moldavian, Gyimes and Burzenland Csangos. In our previous study we found that the Moldavian Csangos have East Asian/Siberian Turkic ancestry components that might be unique in the East-Central European region and might help to better understand the history of Hungarian speaking ethnic groups of the area. Since then, we obtained further Csango samples from Moldavia and from a distinct region of Gyimes, which two Csango subgroups are traditionally different since they live in a degree of isolation not only from other people but also from each other. Here we present the first genomic analysis of Gyimes Csangos, which intended to compare the genetic makeup of these two Csango subgroups using both allele-frequency and haplotype-based methods. The main goal of the study was to investigate the genetic isolation of the Csangos on a genome-wide SNP basis and to assess the isolation of Gyimes Csangos, which in contrast to the Moldavians was not yet studied. Results Our results show that these two Csango groups show slight differences from each other. We confirmed the genetic isolation of Moldavian Csangos and revealed that Gyimes Csangos have a similar, but detectably weaker isolation. In the case of Gyimes Csangos we detected also a stronger East European or presumably Asian derived ancestry. Conclusion The Gyimes Csangos show a degree of genetic isolation comparable to that of the Moldavians. The Asian ancestry that differentiates the Moldavian Csango people from the other East-Central European populations may be present in the Gyimes Csangos in an even higher degree, since Gyimes Csango individuals show a more significant share from that ancestry component.

Abstract Leukemia is a form of cancer that affects the bone marrow and lymphatic system, and it requires complex treatment strategies that vary with each subtype. Due to the subtle morphological differences among these types, monitoring gene expressions is crucial for accurate classification. Manual or pathological testing can be time-consuming and expensive. Therefore, data-driven methods and machine learning algorithms offer an efficient alternative for leukemia classification. This study introduced a novel super learning model that leverages heterogeneous machine learning models to analyze gene expression data and classify leukemia cells. The proposed approach incorporates an entropy-based feature importance technique to identify the gene profiles most significant to the labeling process. The strength of this super learning model lies in its final super learner, Random Forest, which effectively classifies cross-validated data from the candidate learners. Validation on a gene expression monitoring dataset demonstrates that this model outperforms other state-of-the-art models in predictive accuracy. The study contributes to the knowledge regarding the use of advanced machine learning techniques to improve the accuracy and reliability of leukemia classification using gene expression data, addressing the challenges of traditional methods that rely on clinical features and morphological examination.

Background: Staphylococcus aureus is an important pathogen that causes mild to invasive infections in hospitals and the community. Although methicillinsusceptible Staphylococcus aureus (MSSA) isolates continue to cause different infections, there is no data on the genetic backgrounds of the MSSA colonizing or causing infections in Kuwait hospitals. This study aimed to investigate MSSA isolated from patients admitted to Kuwait hospitals for antibiotic resistance and genetic backgrounds to understand their clonal composition. Methods: Consecutive MSSA isolates were collected from single patients during two surveillance periods in 2016 and 2021 in 13 public hospitals. The isolates were characterized using antibiogram, staphylococcal protein A (spa) typing, DNA microarray analysis, and multilocus sequence typing (MLST) using standard protocols. Results: A total of 446 MSSA was cultured from different clinical samples in 2016 (n = 240) and 2021 (n = 206). All isolates were susceptible to vancomycin [minimum inhibitory concentration (MIC) ≤ 2 mg/L], teicoplanin (MIC ≤2 mg/L), linezolid (MIC ≤4 mg/L), ceftaroline (MIC ≤2 mg/L), rifampicin, and mupirocin but were resistant to erythromycin (21.3%), clindamycin (14.0%), gentamicin (3.8%), kanamycin (10.5%), fusidic acid (27.0%), tetracycline (6.9%), trimethoprim (23.1%), and ciprofloxacin (35.2%). Molecular typing identified 155 spa types, dominated by t127 (15.0%), t084 (5.4%), t3841 (5.4%), t267 (2.4%), t442 (2.2%), t091 (2.2%), t021 (2.2%), and t003 (2.2%); 31 clonal complexes (CCs); and 56 sequence types (STs). The majority of the isolates (n = 265; 59.4%) belonged to CC1 (20.6%), CC15 (10.9%), CC22 (5.1%), CC30 (7.6%), CC361 (10.1%), and CC398 (4.7%). Discussion: The MSSA isolates belonged to diverse genetic backgrounds dominated by CC1, CC15, CC22, CC30, CC361, and CC398. The distribution of MSSA clones in 2016 and 2021 showed the stability of these clones over time. The study provides the first comprehensive data on the clonal distribution of MSSA in Kuwait hospitals. [ABSTRACT FROM AUTHOR]

Seborrheic keratosis (SK) is a common benign tumour, often associated with hyperpigmentation. To investigate the mechanism of melanin accumulation in SK, we have conducted comprehensive gene expression and histological analyses. We obtained five pairs of skin samples, including non‐lesional and SK samples, from the backs of three male Japanese participants aged 40–59 years. To examine melanocytes and keratinocytes in SK, three pairs of skin samples were separated by laser capture microdissection into the basal layer and the other layer in the epidermis. We performed a comprehensive gene expression analysis to identify differentially expressed genes between non‐lesional and SK skin, followed by gene ontology and pathway analysis. We found abnormal morphogenesis and cell proliferation in the basal layer, along with increased immune response and impaired cell differentiation and metabolism in the other layer of SK. We focused on cell proliferation and differentiation, as these are directly associated with melanin accumulation. Immunohistochemical analyses of Ki67, keratin 10, and keratin 14 demonstrated the decreases in the proliferation and early differentiation of the epidermis. Contrarily, no significant changes were observed in terminal differentiation markers, filaggrin and loricrin. Although the number of melanocytes was higher in SK than in non‐lesional skin, melanogenic activity showed no difference. These results indicated that melanin accumulation in SK is caused by delayed melanin excretion due to reduced turnover around the basal and spinous layers of the epidermis and melanin production due to an increased number of melanocytes. Our findings provide new insights for therapeutic approaches in SK. [ABSTRACT FROM AUTHOR]

Much research has been conducted to determine how hair regeneration is regulated, as this could provide therapeutic, cosmetic, and even psychological interventions for hair loss. The current study focused on the hair growth effect and effective utilization of fatty oil obtained from Bryde's whales through a high-throughput DNA microarray approach in conjunction with immunohistochemical observations. The research also examined the mechanisms and factors involved in hair growth. In an experiment using female C57BL/6J mice, the vehicle control group (VC: propylene glycol: ethanol: water), the positive control group (MXD: 3% minoxidil), and the experimental group (WO: 20% whale oil) were topically applied to the dorsal skin of the mouse. The results showed that 3% MXD and 20% WO were more effective than VC in promoting hair growth, especially 20% WO. Furthermore, in hematoxylin and eosin-stained dorsal skin tissue, an increase in the number of hair follicles and subcutaneous tissue thickness was observed with 20% WO. Whole-genome transcriptome analysis also confirmed increases for 20% WO in filaggrin (Flg), a gene related to skin barrier function; fibroblast growth factor 21 (Fgf21), which is involved in hair follicle development; and cysteine-rich secretory protein 1 (Crisp1), a candidate gene for alopecia areata. Furthermore, the results of KEGG pathway analysis indicated that 20% WO may have lower stress and inflammatory responses than 3% MXD. Therefore, WO is expected to be a safe hair growth agent. [ABSTRACT FROM AUTHOR]

In recent years, exploring natural compounds with functional properties to ameliorate aging-associated cognitive decline has become a research priority to ensure healthy aging. In the present study, we investigated the effects of Trigonelline (TG), a plant alkaloid, on memory and spatial learning in 16-week-old senescence-accelerated mouse model SAMP8 using an integrated approach for cognitive and molecular biology aspects. After 30 days of oral administration of TG at the dose of 5 mg/kg/day, the mice were trained in Morris Water Maze task. TG-treated SAMP8 mice exhibited significant improvement in the parameters of escape latency, distance moved, and annulus crossing index. Next, we performed a whole-genome transcriptome profiling of the mouse hippocampus using microarrays. Gene ontology analyses showed that a wide range of biological processes, including nervous system development, mitochondrial function, ATP synthesis, and several signaling pathways related to inflammation, autophagy, and neurotransmitter release, were significantly enriched in TG-treated SAMP8 compared to nontreated. Further, a nonlinear dimensionality reduction technique, Uniform Manifold Approximation and Projection (UMAP), was applied to identify clusters of functions that revealed TG primarily regulated pathways related to inflammation, followed by those involved in neurotransmitter release. In addition, a protein–protein interaction network analysis indicated that TG may exert its biological effects through negatively modulating Traf6-mediated NF-κB activation. Finally, ELISA test showed that TG treatment significantly decreased proinflammatory cytokines- TNFα and IL6 and increased neurotransmitters- dopamine, noradrenaline, and serotonin in mouse hippocampus. Altogether, our integrated bio-cognitive approach highlights the potential of TG in alleviating age-related memory and spatial impairment. [ABSTRACT FROM AUTHOR]

Advancements in genomic technologies have paved the way for significant breakthroughs in cancer diagnostics, with DNA microarray technology standing at the forefront of identifying genetic expressions associated with various cancer types. Despite its potential, the vast dimensionality of microarray data presents a formidable challenge, necessitating efficient dimension reduction and gene selection methods to accurately identify cancerous tumors. In response to this challenge, this study introduces an innovative strategy for microarray data dimension reduction and crucial gene set selection, aiming to enhance the accuracy of cancerous tumor identification. Leveraging DNA microarray technology, our method focuses on pinpointing significant genes implicated in tumor development, aiding the development of sophisticated computerized diagnostic tools. Our technique synergizes gene selection with classifier training within a logistic regression framework, utilizing a generalized Fused LASSO (GFLASSO-LR) regularizer. This regularization incorporates two penalties: one for selecting pertinent genes and another for emphasizing adjacent genes of importance to the target class, thus achieving an optimal trade-off between gene relevance and redundancy. The optimization challenge posed by our approach is tackled using a sub-gradient algorithm, designed to meet specific convergence prerequisites. We establish that our algorithm's objective function is convex, Lipschitz continuous, and possesses a global minimum, ensuring reliability in the gene selection process. A numerical evaluation of the method's parameters further substantiates its effectiveness. Experimental outcomes affirm the GFLASSO-LR methodology's high efficiency in processing high-dimensional microarray data for cancer classification. It effectively identifies compact gene subsets, significantly enhancing classification performance and demonstrating its potential as a powerful tool in cancer research and diagnostics. [ABSTRACT FROM AUTHOR]

Glutathione (GSH) has long been recognised for its antioxidant and detoxifying effects on the liver. The hepatoprotective effect of GSH involves the activation of antioxidative systems such as NRF2; however, details of the mechanisms remain limited. A comparative analysis of the biological events regulated by GSH under physiological and oxidative stress conditions has also not been reported. In this study, DNA microarray analysis was performed with four experiment arms including Control, GSH, hydrogen peroxide (HP), and GSH + HP treatment groups. The GSH-treated group exhibited a significant upregulation of genes clustered in cell proliferation, growth, and differentiation, particularly those related to MAPK, when compared with the Control group. Additionally, liver functions such as alcohol and cholesterol metabolic processes were significantly upregulated. On the other hand, in the HP-induced oxidative stress condition, GSH (GSH + HP group) demonstrated a significant activation of cell proliferation, cell cycle, and various signalling pathways (including TGFβ, MAPK, PI3K/AKT, and HIF-1) in comparison to the HP group. Furthermore, several disease-related pathways, such as chemical carcinogenesis–reactive oxygen species and fibrosis, were significantly downregulated in the GSH + HP group compared to the HP group. Collectively, our study provides a comprehensive analysis of the effects of GSH under both physiological and oxidative stress conditions. Our study provides essential insights to direct the utilisation of GSH as a supplement in the management of conditions associated with oxidative stress. [ABSTRACT FROM AUTHOR]

BackgroundStaphylococcus aureus is an important pathogen that causes mild to invasive infections in hospitals and the community. Although methicillin-susceptible Staphylococcus aureus (MSSA) isolates continue to cause different infections, there is no data on the genetic backgrounds of the MSSA colonizing or causing infections in Kuwait hospitals. This study aimed to investigate MSSA isolated from patients admitted to Kuwait hospitals for antibiotic resistance and genetic backgrounds to understand their clonal composition.MethodsConsecutive MSSA isolates were collected from single patients during two surveillance periods in 2016 and 2021 in 13 public hospitals. The isolates were characterized using antibiogram, staphylococcal protein A (spa) typing, DNA microarray analysis, and multilocus sequence typing (MLST) using standard protocols.ResultsA total of 446 MSSA was cultured from different clinical samples in 2016 (n = 240) and 2021 (n = 206). All isolates were susceptible to vancomycin [minimum inhibitory concentration (MIC) ≤ 2 mg/L], teicoplanin (MIC ≤2 mg/L), linezolid (MIC ≤4 mg/L), ceftaroline (MIC ≤2 mg/L), rifampicin, and mupirocin but were resistant to erythromycin (21.3%), clindamycin (14.0%), gentamicin (3.8%), kanamycin (10.5%), fusidic acid (27.0%), tetracycline (6.9%), trimethoprim (23.1%), and ciprofloxacin (35.2%). Molecular typing identified 155 spa types, dominated by t127 (15.0%), t084 (5.4%), t3841 (5.4%), t267 (2.4%), t442 (2.2%), t091 (2.2%), t021 (2.2%), and t003 (2.2%); 31 clonal complexes (CCs); and 56 sequence types (STs). The majority of the isolates (n = 265; 59.4%) belonged to CC1 (20.6%), CC15 (10.9%), CC22 (5.1%), CC30 (7.6%), CC361 (10.1%), and CC398 (4.7%).DiscussionThe MSSA isolates belonged to diverse genetic backgrounds dominated by CC1, CC15, CC22, CC30, CC361, and CC398. The distribution of MSSA clones in 2016 and 2021 showed the stability of these clones over time. The study provides the first comprehensive data on the clonal distribution of MSSA in Kuwait hospitals.

In this article, a new method is employed for maximizing the performance of the Least Absolute Shrinkage and Selection Operator (Lasso) feature selection model. In fact, we presented a novel regularization for the Lasso by employing an approach to find the best regularization parameter automatically which guarantees best performance of the Lasso in DNA microarray data classification. In our experiment, four well-known publicly available microarray datasets including breast cancer, Diffuse Large B-cell Lymphoma (DLBCL), leukemia and prostate cancer were utilized for evaluation the proposed methods. Experimental results demonstrated the significant dominance of the proposed Lasso against other widely used feature selection methods in terms of best features that led to best performance, robustness and stability in microarray data classification. Accordingly, the proposed method is a powerful algorithm for selection of most informative features which can be used for cancer diagnosis by gene expression profiles. [ABSTRACT FROM AUTHOR]

This article, published in the Austral Journal of Veterinary Sciences, discusses a study on the effects of selenium on gene expression in in vitro follicles of ewes. The study found that selenium activated gene expression in the follicles, particularly genes related to the immune system. The researchers identified numerous differentially expressed genes, some of which were involved in apoptosis regulation. The study suggests that selenium treatment affects the transcriptome and has implications for immune processes in ovarian follicles. Additionally, a list of references to related scientific articles is provided, covering topics such as biological trace elements, CD4(+) T cells, ovarian granulosa cell death, selenium, and immune responses. [Extracted from the article]

Methicillin-resistant Staphylococcus aureus (MRSA) is a major pathogen that causes serious infections in healthcare facilities and in communities. The purpose of this study was to investigate MRSA isolates obtained in a tertiary hospital in Kuwait to assess their antibiotic susceptibility profile and clonal composition. Sixty MRSA isolates collected in 2020 were tested through antibiotic susceptibility testing, spa typing, and DNA microarray analysis. All isolates were found to be susceptible to vancomycin (MIC: ≤3 µg/mL), teicoplanin (MIC: ≤3 µg/mL), rifampicin, and mupirocin, but were resistant to fusidic acid (n = 43, 72%), trimethoprim (n = 27, 45%), ciprofloxacin (n = 31, 51.7%), gentamicin (n = 14; 23.3%), kanamycin (n = 20; 33.3%), chloramphenicol (n = 7; 11.7%), tetracycline (n = 17; 28.3%), erythromycin (n = 19; 31.6%), inducible clindamycin (n = 13; 21.7%), and constitutive clindamycin (n = 2; 3.3%). The isolates belonged to 30 spa types and 13 clonal complexes (CCs). The dominant spa types were t304, t442, t311, t688, and t1234, collectively constituting 28.3% of the isolates. The dominant CCs were CC5 and CC6, which together constituted 46.7% of the isolates. This study provides updated research on antibiotic resistance and changes in the clonal composition of MRSA in a Kuwait hospital, including the disappearance of the ST239-MRSA-III clone that was previously the dominant clone in this hospital. [ABSTRACT FROM AUTHOR]

This study by Sasahara et al. explores the role of ephrin A1 in brain arteriovenous malformations (AVM) using DNA microarray analysis, quantitative real-time RT-PCR, and immunohistochemistry. The research identifies significant upregulation of ephrin A1 in AVM, suggesting its potential involvement in the abnormal vascular architecture characteristic of this condition. The study's innovative methodology and thorough exploration of gene expression patterns contribute valuable insights into AVM pathogenesis, highlighting ephrin A1 as a potential therapeutic target. However, the study's limitations include clinical variability among patient samples and the use of draining veins as controls, which may affect the robustness of the findings. Future research should address these limitations by using more homogeneous samples and expanding the investigation to include other ephrin family members. This could provide a broader understanding of ephrin signaling in AVM and guide the development of targeted therapies. [ABSTRACT FROM AUTHOR]

Advancements in genomic technologies have paved the way for significant breakthroughs in cancer diagnostics, with DNA microarray technology standing at the forefront of identifying genetic expressions associated with various cancer types. Despite its potential, the vast dimensionality of microarray data presents a formidable challenge, necessitating efficient dimension reduction and gene selection methods to accurately identify cancerous tumors. In response to this challenge, this study introduces an innovative strategy for microarray data dimension reduction and crucial gene set selection, aiming to enhance the accuracy of cancerous tumor identification. Leveraging DNA microarray technology, our method focuses on pinpointing significant genes implicated in tumor development, aiding the development of sophisticated computerized diagnostic tools. Our technique synergizes gene selection with classifier training within a logistic regression framework, utilizing a generalized Fused LASSO (GFLASSO-LR) regularizer. This regularization incorporates two penalties: one for selecting pertinent genes and another for emphasizing adjacent genes of importance to the target class, thus achieving an optimal trade-off between gene relevance and redundancy. The optimization challenge posed by our approach is tackled using a sub-gradient algorithm, designed to meet specific convergence prerequisites. We establish that our algorithm’s objective function is convex, Lipschitz continuous, and possesses a global minimum, ensuring reliability in the gene selection process. A numerical evaluation of the method’s parameters further substantiates its effectiveness. Experimental outcomes affirm the GFLASSO-LR methodology’s high efficiency in processing high-dimensional microarray data for cancer classification. It effectively identifies compact gene subsets, significantly enhancing classification performance and demonstrating its potential as a powerful tool in cancer research and diagnostics.

Glutathione (GSH) has long been recognised for its antioxidant and detoxifying effects on the liver. The hepatoprotective effect of GSH involves the activation of antioxidative systems such as NRF2; however, details of the mechanisms remain limited. A comparative analysis of the biological events regulated by GSH under physiological and oxidative stress conditions has also not been reported. In this study, DNA microarray analysis was performed with four experiment arms including Control, GSH, hydrogen peroxide (HP), and GSH + HP treatment groups. The GSH-treated group exhibited a significant upregulation of genes clustered in cell proliferation, growth, and differentiation, particularly those related to MAPK, when compared with the Control group. Additionally, liver functions such as alcohol and cholesterol metabolic processes were significantly upregulated. On the other hand, in the HP-induced oxidative stress condition, GSH (GSH + HP group) demonstrated a significant activation of cell proliferation, cell cycle, and various signalling pathways (including TGFβ, MAPK, PI3K/AKT, and HIF-1) in comparison to the HP group. Furthermore, several disease-related pathways, such as chemical carcinogenesis–reactive oxygen species and fibrosis, were significantly downregulated in the GSH + HP group compared to the HP group. Collectively, our study provides a comprehensive analysis of the effects of GSH under both physiological and oxidative stress conditions. Our study provides essential insights to direct the utilisation of GSH as a supplement in the management of conditions associated with oxidative stress.

This study provides a comprehensive perspective on the deregulated pathways and impaired biological functions prevalent in human glioblastoma (GBM). In order to characterize differences in gene expression between individuals diagnosed with GBM and healthy brain tissue, we have designed and manufactured a specific, custom DNA microarray. The results obtained from differential gene expression analysis were validated by RT-qPCR. The datasets obtained from the analysis of common differential expressed genes in our cohort of patients were used to generate protein-protein interaction networks of functionally enriched genes and their biological functions. This network analysis, let us to identify 16 genes that exhibited either up-regulation (CDK4, MYC, FOXM1, FN1, E2F7, HDAC1, TNC, LAMC1, EIF4EBP1 and ITGB3) or down-regulation (PRKACB, MEF2C, CAMK2B, MAPK3, MAP2K1 and PENK) in all GBM patients. Further investigation of these genes and enriched pathways uncovered in this investigation promises to serve as a foundational step in advancing our comprehension of the molecular mechanisms underpinning GBM pathogenesis. Consequently, the present work emphasizes the critical role that the unveiled molecular pathways likely play in shaping innovative therapeutic approaches for GBM management. We finally proposed in this study a list of compounds that target hub of GBM-related genes, some of which are already in clinical use, underscoring the potential of those genes as targets for GBM treatment., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Victor M. Barberá reports financial support was provided by Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunidad Valenciana (FISABIO), Spain. Camino de Juan Romero and Miguel Saceda reports financial support was provided by Carlos III Health Institute, Spain and the Consellería de Innovación, Universidades, Ciencia y Sociedad Digital (Generalitat Valenciana), Spain., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The first trimester of pregnancy is crucial for organ development but also carries a high risk of complications, with early pregnancy loss being the most common. Anomalies in the yolk sac, the first extra-embryonic structure seen by ultrasonography, can indicate severe fetal growth abnormalities and are linked to higher rates of first-trimester loss. This case report details a 38-year-old woman with a history of recurrent pregnancy loss (RPL) who presented with per vaginal bleeding and mild abdominal pain. Transvaginal ultrasonography revealed a yolk sac larger than 10 mm, prompting further genetic investigation. Chromosomal microarray analysis confirmed Trisomy 22. The presence of an enlarged yolk sac, correlated with Trisomy 22, highlights the importance of early detection through sonography and genetic testing. This approach aids in managing RPL by identifying genetic causes, thereby informing pre-conception counseling and future pregnancy management. An abnormal yolk sac size necessitates thorough evaluation, including cytogenetic microarray testing and quantitative fluorescent-polymerase chain reaction analysis, to guide clinical decisions and improve pregnancy outcomes., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Kolate et al.)

• Investigating changes in novel molecular factors and mechanisms in the hippocampus of exercise-induced rats. • Significant identification of aging gene expressions in the hippocampus of wheel-running rats. • Gene expression pattern changes in key categories: cell death and survival, renal necrosis, and cardiovascular disease genes. • Exercise may mitigate aging-related cognitive decline by regulating gene expression in the hippocampus. Recent research has underscored the influence of aging and exercise on brain function. In this study, we aimed to explore alterations in the expression of novel molecular factors and gain insight into underlying molecular mechanisms in the hippocampus of rats engaged in voluntary wheel running. We assessed the expression of aging-related genes in the hippocampus using a high-throughput whole genome DNA microarray approach in rats engaged in voluntary running for four weeks. The results indicated that compared to the control group, wheel running significantly altered the expressions of aging-related genes in the hippocampus. Functional categorization, utilizing pathway-focused gene classifications and disease state-focused gene classifications, along with Ingenuity Pathway Analysis (IPA), revealed changes in expression pattern in major categories of cell death and survival, renal necrosis/cell death, and cardiovascular disease genes. These findings suggest that exercise may mitigate the risk of age-related cognitive decline by regulating of aging-related genes in the hippocampus. Further research is warranted to elucidate the mechanisms driving changes in gene expression and to determine the long-term effects of exercise on brain function. [ABSTRACT FROM AUTHOR]