Circular RNAs (circRNAs) are non-coding RNAs characterized by a strictly closed-loop covalent structure. They are abundantly detected in various cells due to their conserved nature. Studies have reported their potential association with chronic liver disease (CLD), including hepatitis, cirrhosis, and hepatocellular carcinoma (HCC), with possible roles as diagnostic and prognostic markers. This study aimed to analyze the potential use of serum-derived hsa_circ_101555 as a diagnostic tool for CLD without HCC, and to compare it with other known non-invasive parameters for liver disease severity and inflammation. Additionally, it aimed to evaluate its expression among non-HCC CLD patients, CLD with HCC cases reported in our published (phase I) study, and healthy controls.

A cross-sectional study (phase II) was conducted involving 30 clinically, laboratory, and radiologically diagnosed Egyptian non-HCC CLD patients and 30 healthy subjects. The serum expression level of hsa_circ_101555 was measured using real-time polymerase chain reaction. The diagnostic accuracy was assessed through receiver operating characteristic curve analysis, calculating the area under the curve to determine sensitivity and specificity. The study also compared hsa_circ_101555 levels with established non-invasive parameters such as the Child-Turcotte-Pugh and model for end-stage liver disease scores, as well as inflammatory markers like the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio.

hsa_circ_101555 demonstrated high diagnostic accuracy (area under the curve of 0.970) at a cutoff point of 2.088 for differentiating non-HCC CLD patients from healthy controls. Elevated circRNA levels were noted in patients with hepatic encephalopathy and ascites, correlating with advanced liver disease scores (Child-Turcotte-Pugh/model for end-stage liver disease scores). Mean circRNA values were highest in HCC cases, followed by non-HCC CLD patients, and lowest in healthy controls.

Serum-derived hsa_circ_101555 demonstrates high diagnostic accuracy in differentiating non-HCC CLD patients from healthy controls. These findings suggest that hsa_circ_101555 has the potential to serve as a reliable non-invasive biomarker for the early diagnosis of CLD, correlating with disease severity and inflammation markers. Further research with larger sample sizes is warranted to validate its clinical utility and enhance the management of CLD.

Sclerocarya birrea (A. Rich) Hochst (Anacardiaceae) is a plant widely used by traditional healers in several African countries to treat numerous illnesses such as Alzheimer’s disease, schizophrenia, inflammation, infections, arterial hypertension, headaches, and others. This study aimed to determine the therapeutic efficacy of Sclerocarya birrea (S. birrea) against glutamate-induced neurotoxicity.

Thirty naïve white mice (Mus musculus Swiss, Muridae), of both genders and weighing between 18 and 25 g, were used in the experiments. Different doses (102.5, 205, and 410 mg/kg) of the extract and vitamin C (100 mg/kg) were administered to the animals one hour before administration of monosodium glutamate (4 mg/kg) for 15 consecutive days. T-maze and Y-maze tests were conducted over three days to assess the animals’ behavioral performance. After behavioral testing, the animals were sacrificed and their brains removed for analysis of oxidative stress parameters.

S. birrea extract reversed glutamate-induced behavioral alterations by significantly (P < 0.001) reducing the latency to reach the platform in the T-maze and significantly increasing the percentage of spontaneous alternation in the Y-maze. The extract also significantly counteracted (P < 0.001) glutamate-induced oxidative stress parameters. The 102.5 and 205 mg/kg doses of the extract significantly (P < 0.001) reduced catalase and reduced glutathione levels, as well as the increase in malondialdehyde levels induced by glutamate.

S. birrea root bark extract exhibits neuroprotective properties that facilitate memory and ameliorate glutamate-induced cognitive deficits in white mice. The results provide partial justification for the traditional medicinal use of S. birrea extract.

Metaplastic breast carcinoma, a rare entity (<1% of breast neoplasms), lacks comprehensive spectroscopic characterization. This study aimed to address this gap by providing a qualitative and quantitative spectroscopic profile of metaplastic carcinoma in comparison to ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC).

A retrospective analysis was conducted on archival tissue blocks of metaplastic carcinoma (n = 10), DCIS (n = 12), and IDC (n = 31). Sections were stained with hematoxylin and eosin for histological confirmation. Attenuated total reflectance Fourier-transform infrared spectroscopy was performed on adjacent unstained sections, with normal breast tissue (n = 10) serving as the control. Spectral data were analyzed using t-tests to identify significant differences in peak intensities and ratios. Hierarchical clustering analysis and receiver operating characteristic curves were generated to assess the diagnostic potential of selected spectral features.

Spectral analysis revealed that mean peak intensities were generally lower in all carcinoma subtypes compared to normal breast tissue. Specific ratios, including A1237/A1080 (phosphate; p < 0.01), A1043/1543 (glycogen; p < 0.01), and A1080/A1632 (nucleocytoplasmic index; p < 0.03), were significantly elevated in carcinomatous tissues. Receiver operating characteristic analysis identified peak 3,280 (area under the curve (AUC) = 0.93–0.96) as highly effective in differentiating normal from carcinomatous tissues. Peak 2,922 showed specificity for distinguishing normal tissue from IDC (AUC ≈ 0.7). Peak 1,744 effectively discriminated between DCIS and metaplastic carcinoma (AUC = 0.7). The ratio 1,080/1,632 (nucleocytoplasmic ratio) demonstrated exceptional diagnostic accuracy, distinguishing normal from carcinomatous tissues (AUC ≈ 1.0), DCIS from IDC (AUC ≈ 0.86), and DCIS from metaplastic carcinoma (AUC ≈ 0.8).

Attenuated total reflectance Fourier-transform infrared spectroscopy, particularly using peak 3,280 (Amide A) and the 1,080/1,632 ratio (nucleocytoplasmic index), offers a promising approach for discriminating between normal breast tissue and carcinoma, as well as differentiating pre-IDC from metaplastic carcinoma. These spectral markers demonstrate both statistical significance and diagnostic potential.

Endometrial polyp (EMP) is one of the most common diagnoses in the evaluation of women with abnormal uterine bleeding. Understanding the malignancy risk associated with EMPs and related risk factors is essential for guiding both pathology practice and clinical management. This study aimed to explore risk factors for malignancy in EMPs.

The pathology database was searched for women diagnosed with EMP between 2021 and 2022. Patient age, polyp size, background endometrium, recurrence, and (if applicable) cancer types were recorded. Immunohistochemistry (IHC) for p53 and p16 was performed on selected cases. Risk factors for malignancy were analyzed using Chi-square and analysis of variance tests.

Among the 740 EMP cases analyzed, 94% were benign, 2% were premalignant, and 4% were malignant. The median patient age was 54 years (range: 19–92). Minimal serous carcinoma (n = 14, 2%) was the most prevalent cancer. Among the 52 cases with p53 IHC, 38 were diagnosed as benign, supported by a wild-type p53 pattern, while 14 were diagnosed as serous carcinoma, supported by a mutant p53 pattern. Malignant polyps were found to be significantly associated with advanced age and malignant background endometrium (p < 0.001). Large size and recurrence were not identified as significant risk factors.

EMPs carry a low risk of malignancy, which is not significantly influenced by the polyp’s size or its recurrence. Our findings highlight the significantly elevated risk of malignancy in elderly patients and the importance of p53 IHC in improving diagnostic accuracy.

Uterine fibroids (UFs) are common hormone-dependent tumors with a complex etiology involving both genetic and environmental factors. This study aimed to investigate, for the first time, the associations between loci from genome-wide association studies (GWAS) and environmental risk factors in UF development, with a particular focus on gene–environment interactions.

DNA samples from 737 women with UF and 451 healthy controls were genotyped for ten UF-associated GWAS single nucleotide polymorphisms (SNPs) using probe-based polymerase chain reaction in this case-control study.

SNP rs66998222 (LOC102723323, G/A) was associated with decreased UF risk in the total sample (odds ratio (OR) = 0.81, p = 0.038) and in patients with a history of induced abortion (OR = 0.70, p = 0.009). SNP rs11031731 (THEM7P, WT1, G/A) increased UF risk overall (OR = 1.39, p = 0.01), and in women with abortion history (OR = 1.60, p = 0.008) or without pelvic inflammatory disease (OR = 1.43, p = 0.02). SNPs rs641760 (PITPNM2, C/T) and rs2553772 (LOC105376626, G/T) showed protective effects depending on abortion history. SNP rs1986649 (FOXO1, C/T) was associated with later UF onset (p = 0.049) and slower growth (p = 0.017). GWAS loci influence UF-related genes involved in proliferation, inflammation, and hormone metabolism, underscoring their pathogenic role.

Induced abortions and inflammation modify the effects of GWAS-identified UF risk loci, with allele-specific impacts on hormonal, inflammatory, and repair pathways. Replication in diverse cohorts is needed to validate these population-specific effects.

Chimeric antigen receptor (CAR)-T cell therapy faces significant challenges in treating solid tumors, including immune evasion, suppressive tumor microenvironments, and on-target/off-tumor toxicity, which limit its clinical efficacy. Although it has revolutionized treatment for hematological malignancies, these obstacles hinder its broader application in solid tumors. Nanotechnology offers innovative strategies to address these limitations through enhanced delivery, localization, and control. This review summarizes recent advances in nanotechnology-assisted CAR-T cell therapies for gynecologic cancers, with a particular focus on messenger RNA (mRNA)-based delivery systems, lipid nanoparticles, hydrogels, and external activation techniques such as photothermal and acoustogenetic modulation. The integration of nanotechnology, especially mRNA-based delivery systems, holds transformative potential for overcoming these barriers. mRNA enables transient, non-integrating expression of CARs, meaning the genetic modifications are temporary. This improves safety and allows flexible control over treatment intensity, while rational sequence optimization (e.g., codon usage, guanine-cytosine content, secondary structure) enhances mRNA stability and protein translation efficiency. Lipid nanoparticles, the leading delivery platform, can be engineered for cell-type specificity and tissue targeting through modulation of their components and surface functionalization. Recent innovations, including siloxane-modified lipid nanoparticles, injectable hydrogels, and photothermal or acoustogenetic activation strategies, enable precise spatiotemporal control of CAR-T cell function in vivo. In ovarian cancer, preclinical studies targeting nfP2X7 and employing multifunctional nanoparticles have demonstrated synergistic efficacy and tumor-specific delivery. This review highlights how nanotechnology platforms can be integrated with CAR-T cell therapies to enhance safety, precision, and therapeutic outcomes in ovarian cancer.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is now considered to be among the most prevalent chronic liver diseases worldwide. Its comprehensive management encompasses multiple stages, including risk assessment, early detection, stratified intervention, and long-term follow-up. Among these, improving diagnostic accuracy and optimizing individualized therapeutic strategies remain key challenges in both research and clinical practice. In recent years, artificial intelligence and smart devices have developed rapidly and have gradually been applied in the medical field, offering novel tools and pathways for MASLD risk stratification, non-invasive diagnosis, therapeutic evaluation, and patient self-management. This review summarizes the current applications of artificial intelligence and smart devices in MASLD care, highlights their benefits and limitations, and discusses future directions to support precision diagnosis and treatment strategies.