Immunization against human papillomavirus (HPV), particularly with a single-dose vaccine, offers a cost-effective strategy for cervical cancer prevention. This study aimed to evaluate the seroprevalence following a single-dose bivalent HPV vaccine among adolescent girls in Bangladesh and to examine its association with sociodemographic characteristics.

A cross-sectional study was conducted among 648 adolescent girls (aged nine to fifteen years) in Dhaka, Bangladesh, who received a single dose of the bivalent HPV vaccine in November 2019. Participants were recruited from ten local schools. At 36 months post-vaccination, blood samples were analyzed for HPV16/18 L1-specific immunoglobulin G using enzyme-linked immunosorbent assay. Sociodemographic data were collected and analyzed using logistic regression.

Most participants were aged nine to thirteen years (82.4%), with a mean age of 11.89 ± 1.59 years. The overall seroprevalence was 72.8% for HPV16 and 82.4% for HPV18. Seropositivity for HPV16 was significantly lower among participants aged 14–15 years [adjusted odds ratio (aOR) = 0.61; 95% confidence interval (CI): 0.39–0.95; p = 0.020] and those in grades nine to ten (aOR = 0.50; 95% CI: 0.28–0.89; p = 0.004). For HPV18, significantly reduced odds of seropositivity were observed among participants from households with monthly incomes up to Taka 10,000 (aOR for Taka 10,001–20,000 = 0.41; 95% CI: 0.26–0.67; p < 0.001; aOR for Taka 20,001–50,000 = 0.21; 95% CI: 0.11–0.40; p < 0.001).

A single-dose bivalent HPV vaccine induces sustained immunity in Bangladeshi adolescent girls, with lower HPV16 seropositivity among older girls and those in higher grades, and higher HPV18 seropositivity is linked to lower household income.

In the post-genomic era, long non-coding RNAs (lncRNAs) have emerged as critical regulators in various cancers and hold potential as minimally invasive diagnostic biomarkers. This study aimed to perform microarray analysis of the peripheral blood mononuclear cell (PBMC) transcriptome to evaluate differential lncRNA expression in women with luminal A breast cancer.

A one-color microarray analysis was conducted using SurePrint G3 Human Unrestricted 8×60K arrays and a SureScan Microarray Scanner (Agilent Technologies, USA). The study cohort comprised 16 participants: eight patients diagnosed with luminal A breast cancer and eight healthy controls. Bioinformatic analysis was performed using the “limma” and “tidyverse” packages in the R statistical environment. Functional enrichment analysis was conducted to identify significantly differentially expressed gene clusters. The false discovery rate-adjusted p-value (padj) was applied to ensure methodological rigor. Associations between lncRNAs and disease progression were explored using the LncRNADisease 2.0 database.

Differential expression was observed for long intergenic non-coding (LINC), LOC, and antisense RNA genes. Notably, LINC RNA 974 (LINC00974) exhibited significant differential expression (log fold change > |1.5|, padj < 0.05) after multiple comparison correction. Analysis using the LncRNADisease 2.0 database revealed associations between LINC and antisense RNAs and other oncological disorders.

This study is the first to demonstrate differential lncRNA expression in PBMCs of patients with luminal A breast cancer. Despite the limited sample size, the study demonstrates statistically significant differences between groups, highlighting the potential of PBMC-derived lncRNAs as minimally invasive biomarkers. These findings enhance our understanding of the utility of PBMC-derived lncRNAs as biomarkers for breast cancer.

Acute liver failure (ALF) is a disorder with various etiologies. Although the causes leading to this disruptive condition are well documented in published ALF cohorts, there is significant concern among patients who experience ALF with indeterminate causes, an issue requiring thorough analysis. This review aimed to analyze cohort studies on ALF with a focus on unknown causes leading to classification as indeterminate ALF. The analysis revealed that, among 67 worldwide adult and pediatric ALF cohorts, indeterminate causes of ALF ranged from 2% to 100%, with an average of 30%. Among the 13 pediatric ALF cohorts, the corresponding range was 22% to 100%, with an average of 47%, while among the 55 adult ALF cohorts, the range was 2% to 78%, with an average of 26%. The percentage values were higher in pediatric cohorts due to the higher incidence of rare genetic causes compared to adult patients. Notably, higher rates of indeterminate causes were found in cohorts studied before the availability of diagnostic serologic screening parameters and polymerase chain reaction techniques for various hepatitis virus infections. Patients with indeterminate ALF may not have received a specific treatment that, if effective, could have helped prevent liver transplantation. It is concluded that, in future cases, all efforts must be undertaken to clearly establish the cause of severe liver injury, enabling effective therapy when available and helping reduce the risk of progression to ALF and the need for liver transplantation.

Recent advancements in cancer immunotherapy have highlighted glypican-3 (GPC3) as a prominent target for treating hepatocellular carcinoma (HCC). However, approximately 10% to 30% of HCC patients exhibit low or absent GPC3 expression on the surface of tumor cells, which limits the feasibility of GPC3-targeted therapies. Consequently, it is essential for patients to undergo pre-diagnostic assessments of GPC3 expression in tumor cells to evaluate their suitability for GPC3-directed therapy. Although various methods have been developed to specifically detect GPC3 as a biomarker for treatment and prognosis, the diagnostic approaches currently employed in clinical studies remain relatively limited. Here, we provide a comprehensive overview of the clinical development of GPC3-targeted therapeutics, clinical trials in GPC3-positive HCC, and current methods for detecting GPC3 expression, highlighting their advantages and limitations. Furthermore, we explore the potential of integrating targeted therapy with various GPC3 detection modalities tailored to different pathological stages. This integration not only provides insights into the selection of effective methods for detecting GPC3 expression but also has the potential to significantly improve the clinical outcomes of patients with liver cancer. By simultaneously assessing the advantages and disadvantages of these methods, this review aims to establish a theoretical foundation for the clinical selection of appropriate GPC3 detection strategies for targeted therapy.

While metabolic dysfunction-associated steatotic liver disease (MASLD) is associated with obesity, the cause of its rapidly rising prevalence is not well understood. In this study, we aimed to examine the association between arsenic exposure and MASLD in humans.

Urinary inorganic arsenic data from the National Health and Nutrition Examination Survey, 2011–2020, were used. These were combined with death certificate data from the National Death Index of the National Center for Health Statistics to ascertain mortality rates. Weighted linear regression and chi-squared analysis were performed.

The analysis included 6,386 participants after exclusions. The mean urinary arsenic level was 5.92 µg/L in participants with MASLD versus 5.59 µg/L in those without. Alanine aminotransferase levels exhibited a statistically significant increasing trend across both continuous arsenic levels and arsenic quintiles. A statistically significant upward trend was observed for the income-to-poverty ratio and body mass index but not for education status. MASLD prevalence was highest among the white population, while an increasing trend was observed in the Hispanic population over the years (p < 0.001). The proportion of Mexican Americans increased to 12.6% in the MASLD group versus 8.09% in the non-MASLD cohort (p < 0.001). There was a statistically significant increase in the odds of MASLD across arsenic exposure levels, with individuals in the highest quintile having a 32% greater likelihood compared to those in the lowest quintile (p-trend = 0.002). The odds further increased to 55% in the highest quintile (odds ratio 1.55, 95% confidence interval: 1.19–2.03; p-trend < 0.001). MASLD was more prevalent in females than males (57.9% vs. 47.6%; p < 0.001), and the mean age increased from 46.9 years to 49.9 years (p = 0.016).

Our findings reveal a positive association between urinary arsenic exposure and MASLD, with increasing trends particularly observed among Hispanics and those with higher income-to-poverty ratios and body mass index.

Given the high burden of hepatocellular carcinoma (HCC), risk stratification in patients with cirrhosis is critical but remains inadequate. In this study, we aimed to develop and validate an HCC prediction model by integrating radiomics and deep learning features from liver and spleen computed tomography (CT) images into the established age-male-ALBI-platelet (aMAP) clinical model.

Patients were enrolled between 2018 and 2023 from a Chinese multicenter, prospective, observational cirrhosis cohort, all of whom underwent 3-phase contrast-enhanced abdominal CT scans at enrollment. The aMAP clinical score was calculated, and radiomic (PyRadiomics) and deep learning (ResNet-18) features were extracted from liver and spleen regions of interest. Feature selection was performed using the least absolute shrinkage and selection operator.

Among 2,411 patients (median follow-up: 42.7 months [IQR: 32.9–54.1]), 118 developed HCC (three-year cumulative incidence: 3.59%). Chronic hepatitis B virus infection was the main etiology, accounting for 91.5% of cases. The aMAP-CT model, which incorporates CT signatures, significantly outperformed existing models (area under the receiver-operating characteristic curve: 0.809–0.869 in three cohorts). It stratified patients into high-risk (three-year HCC incidence: 26.3%) and low-risk (1.7%) groups. Stepwise application (aMAP → aMAP-CT) further refined stratification (three-year incidences: 1.8% [93.0% of the cohort] vs. 27.2% [7.0%]).

The aMAP-CT model improves HCC risk prediction by integrating CT-based liver and spleen signatures, enabling precise identification of high-risk cirrhosis patients. This approach personalizes surveillance strategies, potentially facilitating earlier detection and improved outcomes.

The COVID-19 pandemic profoundly impacted university students, presenting multifaceted challenges including the abrupt transition to virtual learning and significant disruptions to emotional well-being and dietary habits. This study aimed to investigate the dietary and nutritional characteristics associated with academic stress among Mexican university students during the COVID-19 lockdown.

This cross-sectional study was conducted with a sample of 114 university students in Mexico. Participants completed a self-reported questionnaire assessing dietary patterns, nutritional intake, and academic stress levels. Informed consent was obtained from all participants prior to data collection.

Among study participants (n = 114), 57.8% experienced moderate academic stress, while 25.7% reported high academic stress during the COVID-19 lockdown. Notably, 13.5% of students demonstrated food cravings that were significantly associated with increased consumption of red and fatty meats (P = 0.030) and sausages (P = 0.017). A negative virtual education experience was associated with food cravings towards high-calorie and saturated-fat foods (P = 0.014), as well as elevated academic stress levels (P = 0.009). Furthermore, high academic stress levels were positively associated with food cravings (P = 0.020), particularly towards carbohydrate-rich foods (P = 0.037).

The COVID-19 lockdown substantially disrupted the dietary habits and nutritional status of university students, with academic stress serving as a significant mediating factor.

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.

Fiberoptic bronchoscopy involves various topical airway anesthesia protocols, which can impact patient comfort, procedural ease, and overall outcomes. This study aimed to compare pre-procedure lignocaine spray (PPL) and spray-as-you-go (SAYG) airway anesthesia in terms of patient discomfort and operator comfort during fiberoptic bronchoscopy.

A single-blind randomized controlled trial was conducted at the Pulmonology Department of Shaikh Zayed Hospital, Lahore, Pakistan, from March 2021 to March 2022. Fifty participants were randomly assigned to two groups (n = 25 each). Standard procedural sedation with midazolam and 2 mL of 4% lignocaine spray in the oropharynx was used to suppress the gag reflex. Additionally, 2% lignocaine spray was administered during the procedure according to body weight (3 mg/kg) via oral scope insertion. Cough severity, pain perception, and operator comfort were assessed using the Visual Analogue Scale, Faces Pain Rating Scale, and a 4-point Likert scale, respectively.

Demographic characteristics were comparable between the groups, with a minor age difference (PPL: 53.25 years vs. SAYG: 50.88 years, p = 0.017). No significant differences were observed in pain perception, cough scores, or procedure duration between the PPL and SAYG groups. Operator comfort scores showed a trend favoring PPL (60% rated as “comfortable” or “very comfortable” vs. 28% in SAYG), though the difference was not statistically significant (p = 0.108).

Both PPL and SAYG topical airway anesthesia methods demonstrated similar effectiveness in pain control, cough suppression, operator comfort, and procedure duration. There was a slight, non-significant preference for PPL in operator comfort. These findings suggest that either technique may be effectively used, with potential implications for procedural efficiency and patient outcomes.

Artificial intelligence (AI) is profoundly transforming the paradigm of solid tumor drug development. By integrating multi-omics data, spatial transcriptomics, and advanced computational models, AI has significantly accelerated the discovery and validation of new targets, compressing the traditional ten-year research and development cycle to two to three years. Generative AI platforms have optimized small molecule inhibitors, biologics, and messenger RNA vaccines, achieving breakthroughs in overcoming tumor heterogeneity, improving efficacy, and predicting drug resistance. However, clinical translation still faces challenges such as data bias, algorithm transparency, and the validation gap between models and real-world human experience. This review aims to systematically elaborate on the transformative role of AI in solid tumor drug development and to promote interdisciplinary cooperation as well as the construction of ethical frameworks to enable the full realization of precision oncology.