Lung cancer remains the leading cause of cancer-related mortality worldwide. Early detection of pulmonary nodules is crucial for timely diagnosis and effective treatment. Conventional computer-aided detection systems have shown limitations, including high false-positive rates and low sensitivity. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have shown great potential in improving the accuracy and reliability of nodule detection and classification. This study aimed to develop and evaluate an automatic method for lung nodule detection and classification using a CNN-based architecture applied to computed tomography images from the publicly available LIDC-IDRI database.

This retrospective study was conducted on 82 patients (10,496 computed tomography slices) selected from the LIDC-IDRI database. The proposed method consists of five main steps: image preprocessing, lung parenchyma segmentation using Otsu’s thresholding and morphological operations, detection of nodule candidates, feature extraction, and classification using a CNN model. The CNN architecture includes two convolutional layers (20 and 30 filters, 3×3 kernel), ReLU activation, max-pooling layers, and a Softmax output layer. The network was trained with a mini-batch size of 32 for 50 epochs using the Stochastic Gradient Descent with Momentum optimizer (learning rate = 0.001, momentum = 0.9). Model performance was evaluated in terms of sensitivity, specificity, precision, and accuracy.

The proposed CNN model successfully detected pulmonary nodules and achieved accurate classification between benign and malignant nodules. On the LIDC-IDRI dataset, the model achieved a sensitivity of 98.7%, specificity of 97.5%, precision of 97.9%, and accuracy of 98.4%. Comparative analysis with recent studies, including hybrid CNN-long short-term memory and ResNet-based models, demonstrated that the proposed method provides competitive performance while maintaining lower computational complexity. The classification of nodule subtypes (solid, partially frosted, totally frosted) showed satisfactory discrimination results.

The proposed CNN-based system demonstrates the feasibility and robustness of deep learning for automatic lung nodule detection and classification. Despite strong results, the study acknowledges limitations such as single-database validation and a relatively small training size. Future work will focus on validating the model across other datasets (e.g., ELCAP, NELSON) and optimizing multi-class classification performance to enhance generalizability and clinical applicability.

Hepatocellular carcinoma (HCC) remains challenging to treat in advanced stages, primarily due to the development of resistance to sorafenib. There is an urgent need for novel therapeutic strategies to overcome this resistance. This study aimed to investigate the potential of oleanolic acid (OA), a natural hepatoprotective compound, in mitigating sorafenib resistance and elucidate its underlying molecular mechanisms.

Sorafenib-resistant Huh7 and HepG2 cell lines were established to mimic the resistant phenotype. The effects of OA on these cells were evaluated by assessing cell invasion, migration, and sensitivity to sorafenib. Gene expression analysis was conducted to identify molecular changes induced by OA treatment, with a focus on fabp3 expression.

Oleanolic acid significantly inhibited the invasive and migratory capabilities of sorafenib-resistant Huh7 and HepG2 cells (p < 0.01). Furthermore, OA treatment downregulated fabp3 expression and restored the cells’ sensitivity to sorafenib.

Oleanolic acid shows promise as an adjunct therapy for overcoming sorafenib resistance in HCC. By reducing cell aggressiveness and restoring drug sensitivity, OA may enhance the therapeutic efficacy of current treatments for advanced HCC.

Escalating antimicrobial resistance is a global threat, emphasizing the need to explore alternative treatment options. Hence, we aimed to explore the in-vitro activity of ceftazidime-avibactam (CAZ-AVI) in clinical isolates of carbapenem-resistant gram-negative bacteria.

This was an observational, cross-sectional study conducted at the Microbiology Department of Indus Hospital, Karachi, Pakistan, from January 2023 to October 2024. Carbapenem-resistant gram-negative rods isolated from clinical specimens received from the outpatient, emergency, and inpatient departments were included. Consecutive, non-probability sampling was employed for the collection of isolates. Identification of the organisms was confirmed using API® ID strips, and antimicrobial susceptibility for carbapenems and CAZ-AVI was determined via the Kirby-Bauer disc diffusion method.

A total of 158 bacterial isolates were characterized as carbapenem-resistant. Of these, 92 (58%) were Enterobacterales, and 66 (42%) were Pseudomonas aeruginosa. CAZ-AVI was susceptible in 17 (11%) of the isolates, of which four (24%) were Klebsiella spp. and Escherichia coli each, and nine (52%) were P. aeruginosa. CAZ-AVI-susceptible strains were predominant among patients aged 26–50 years (n = 6; 35%), most of whom were females (n = 10; 59%) and inpatients (n = 8; 47%). Clinical samples from patients with urinary tract infections grew the most CAZ-AVI-susceptible strains (n = 9; 53%).

Our study demonstrated low CAZ-AVI susceptibility in our carbapenem-resistant gram-negative bacterial strains. Understanding regional antimicrobial patterns in multidrug-resistant bacteria is crucial for the effective use of CAZ-AVI, along with the strict implementation of strategies for controlling antimicrobial resistance.

Lung Squamous cell carcinoma (LSCC) represents the second most common non-small cell lung cancer. Although studies identified adenocarcinoma-like driver mutations in LSCC using next-generation sequencing (NGS), the disease is challenging to treat due to the limited number of detectable mutations for targeted drug therapy. This study aimed to evaluate the mutation profiles of LSCC detected by NGS to assess the relationships between different driver mutations and clinicopathological parameters.

NGS with a panel of 72 cancer-related genes was used to evaluate the driver mutation profiles of 41 lung resection specimens from patients with LSCC at the Molecular Pathology Laboratory of Aydın Adnan Menderes University in Türkiye. Clinical and histopathological features were recorded for analysis.

Detection of 94 mutations in 23 genes in DNA extracted from the tissue samples of 36 patients revealed that the most prevalent mutations were TP53 (30.85%), NF1 (20.20%), PTEN (11.70%), PIK3CA (5.31%), FBXW7 (4.25%), KRAS (3.20%), respectively. We identified statistically significant relationships between PIK3CA and lower mean age (p = 0.007) and between PTEN and mild inflammatory reaction (p = 0.004). PTEN alterations showed a trend toward central tumor localization (p = 0.13), NF1 alterations toward visceral pleural involvement (p = 0.09), and PIK3CA alterations toward severe inflammatory reaction (p = 0.053). Similar trends were observed with advanced pathological T stage (p = 0.09) and N stage (p = 0.057) according to the TNM staging system.

Our study highlights the importance of assessing mutation profiles in LSCC patients to identify driver mutations as potential therapeutic targets. Certain histopathological features are associated with these mutations, serving as indicators for treatment and follow-up decisions.

Coronary heart disease is an ischemic condition characterized by vascular stenosis or obstruction caused by coronary atherosclerosis, resulting in myocardial ischemia, hypoxia, or necrosis. It is one of the leading causes of death in both urban and rural populations in China. Safflower yellow pigments, the main active components of the traditional Chinese herbal medicine safflower, are primarily composed of quinochalcone compounds, including hydroxysafflor yellow A and anhydrosafflor yellow B—of which hydroxysafflor yellow A is the principal component. Studies have demonstrated that these pigments can improve myocardial ischemia, reduce ischemia-reperfusion injury, alleviate atherosclerotic damage, and address risk factors associated with coronary heart disease. This review aimed to systematically and comprehensively summarize the mechanisms of action of safflower yellow pigments and their active components in the context of coronary heart disease and its related risk factors.

Philadelphia chromosome-positive (Ph+) B-lymphoblastic leukemia/lymphoma (B-ALL/LBL) is an aggressive hematologic malignancy driven by the BCR::ABL1 fusion. While many cases respond well to treatment, some patients exhibit persistent BCR::ABL1 expression after therapy, presenting significant diagnostic challenges.

We present the case of a seven-year-old girl diagnosed with Ph+ B-ALL. Despite low percentages or negative results for blasts post-treatment, molecular and cytogenetic studies persistently detected high levels of BCR::ABL1, suggesting a high disease burden at the genetic level. This discordance supported multilineage involvement and the potential for retrospective revision of the initial diagnosis to lymphoblast crisis of chronic myeloid leukemia (LBC-CML).

Classifying such cases as de novo Ph+ B-ALL with multilineage involvement or LBC-CML is challenging, as there is currently no consensus among experts. Further studies are necessary to clarify the distinction, given the different management strategies and treatment responses between these two conditions.

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.

Actively identifying the risk factors and predictive indicators associated with portal vein thrombosis (PVT) in liver cirrhosis (LC) can enable early diagnosis and treatment, which is of great significance for prolonging the survival of patients with LC. Hemodynamic disturbances, advanced LC, vascular endothelial injury, and mutations in thrombophilic genetic factors are established risk factors for PVT-LC. Venous dilatation and decreased blood flow velocity contribute to hemodynamic disturbances. The severity of LC can be assessed by the degree of portal hypertension, liver metabolic function biomarkers, and validated liver scoring systems. Iatrogenic interventions, endotoxemia, and metabolic syndrome may induce vascular endothelial injury and hypercoagulability, the latter of which can be quantified via coagulation-anticoagulation-fibrinolysis biomarkers. Mutations in thrombophilic genetic factors, such as Factor V Leiden, MTHFR C667T, and JAK2 V617F, disrupt coagulation-anticoagulation homeostasis and predispose patients to PVT-LC. This review specifically focuses on comprehensively delineating established risk factors and predictive indicators for PVT-LC, thereby providing a theoretical foundation for the construction of clinically applicable PVT predictive models to guide early interventions and improve the prognosis. Future research should further validate the associations between recently proposed risk factors and PVT-LC, while simultaneously establishing cutoff values for indicators with robust predictive value to construct a clinically applicable PVT prediction framework.

This review explores the integration of complexity science—specifically, the biological holographic phenomenon and chaos-fractal theory—with the fundamental principles of traditional Chinese medicine (TCM) to optimize perioperative recovery. It examines how these theories provide a scientific foundation for developing a digital TCM diagnosis and treatment platform. Key topics discussed include the application of digital four-diagnosis technology, visualization of perioperative Yin-Yang states, and artificial intelligence-driven biomarker discovery. By quantifying and digitizing core TCM concepts, this approach enables their incorporation into Enhanced Recovery After Surgery protocols. Ultimately, the review highlights the potential of integrating TCM with Western medicine to advance personalized postoperative management, offering both theoretical insights and practical strategies for improving perioperative care.

Patients with chronic hepatitis B virus (HBV) infection in the immune-tolerant phase may still experience hepatic inflammation and disease progression, and could benefit from early antiviral treatment. This study aimed to investigate changes in the cumulative hepatitis B surface antigen (HBsAg)/HBV DNA ratio in immune-tolerant patients during the transition to the immune-active phase, and to evaluate its potential in predicting the risk of disease progression.

This longitudinal study included 127 untreated immune-tolerant patients, who were followed for up to 10 years. An independent cohort of 109 subjects was retrospectively enrolled for external validation. The relationship between the cumulative HBsAg/HBV DNA ratio and the duration of immune tolerance or transition to the immune-active phase was examined. The predictive value of the ratio was assessed and validated.

The relationship between the cumulative HBsAg/HBV DNA ratio and disease progression risk showed a non-linear pattern: below a ratio of 1.791, the risk of disease progression decreased rapidly as the ratio increased; above 1.791, the risk plateaued. The area under the curve for predicting disease progression was 0.67, 0.64, and 0.85 for cumulative HBsAg, HBV DNA, and the HBsAg/HBV DNA ratio, respectively. Multivariable Cox regression analysis revealed the cumulative HBsAg/HBV DNA ratio as an independent predictor of disease progression, with higher ratios associated with a lower risk. Prediction models incorporating this ratio were developed and externally validated, demonstrating strong performance and clinical utility.

The cumulative HBsAg/HBV DNA ratio is an independent factor influencing the duration of immune tolerance and shows superior predictive performance. It may serve as a valuable marker for assessing the risk of disease progression in patients with chronic HBV infection.