Widely used breast cancer risk prediction tools are predominantly based on data from Western countries, and their predictive efficacy in Asian women is often questioned, partly due to ethnic differences in risk factors. This study aimed to develop and temporally validate the Macau Breast Cancer Risk Stratification Tool (MBC-RST), based on non-laboratory-detectable indicators. This multi-stage study involved the development and temporal validation of the MBC-RST. Phase 1 involved a literature review and retrospective analysis of 14,111 local health questionnaires to create an initial 17-item tool (MBC-RST_Version 1) with expert consultation. In Phase 2, this version was pilot-tested on 10,691 women, while a more detailed cross-sectional survey (MBC-RS) was administered to 1,321 women. Machine learning (specifically LightGBM) and statistical analyses were used to identify additional predictive factors, leading to an optimized 21-item tool (MBC-RST_Version 2). Phase 3 involved the launch and performance evaluation of MBC-RST_Version 2 using data from 9,985 new participants. The initial retrospective analysis identified significant risk factors including age (OR = 1.09), delayed age at menarche (OR = 0.90), presence of a breast lump (OR = 22.2), and unilateral nipple discharge (OR = 2.14). The subsequent factor analysis and machine learning analysis on prospective data identified 'pregnancy history,' 'age at first childbirth,' 'number of miscarriages,' and 'exercise intensity' as highly important predictors. These factors were integrated into the final 21-item tool. The final MBC-RST_Version 2, evaluated on a total of 25,476 participants, demonstrated a sensitivity of 86.7

Molecular heterogeneity in hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC) complicates patient stratification. Here, we perform integrative proteomic analysis on 272 early stage HBV-HCC tumors from four East Asian cohorts, uncovering two robust molecular subtypes. Group B (n = 53) is associated with hyperproliferation, oncogenic signaling, and significantly poorer overall (P <0.001) and relapse-free survival (P <0.05). In contrast, group A (n = 219) is enriched in differentiation and metabolic pathways. These findings are validated by matched transcriptomics (n = 108), aligning with established classifications. We identify subtype-specific protein signatures, with CD46, HNF1A, and ATP1B1 exclusively expressed in the aggressive group B. Finally, computational drug sensitivity prediction, validated by molecular docking, nominates Sunitinib as a potential therapy for group B patients. Our work provides a proteomic framework for improved prognostication and targeted therapy in high-risk HBV-HCC.

Monitoring the effectiveness of COVID-19 vaccination is critical for understanding if the vaccinated population, especially the elderly, is adequately protected from the emergence of new SARS-CoV-2 variants. This study aimed to investigate the effects of COVID-19 vaccination on the severity of symptoms and mortality in hospitalized geriatric patients during the Omicron BF.7 surge in Macao. Data from electronic health records and vaccination registry of inpatients aged 60 years or above admitted to Kiang Wu Hospital from 12 December 2022 to 12 March 2023 were retrospectively analyzed. The study involved 848 people, including 426 vaccinated and 422 unvaccinated individuals. The mean CXR scores (8.95 ± 9.49 vs. 11.41 ± 10.81, p < 0.001) and the mean MEWS scores (0.96 ± 2.01 vs. 1.49 ± 2.45, p < 0.001) were lower in the vaccinated group. By comparing the dose counts, no significant difference was seen in the odds of death. Based on the time of the last vaccination, 128 people were categorized as complete and 298 as incomplete vaccination. The complete vaccination group showed a 54% (95% CI 0.23–0.91) reduction in mortality risk (p = 0.026). The study findings not only reconfirm the effectiveness of COVID-19 vaccination but, more importantly, highlight the importance of vaccination timing to maximize vaccines’ protective effect.

Introduction: Fetal growth restriction (FGR) is associated with a higher risk of perinatal morbidity and mortality, as well as long-term health issues in newborns. Currently, there is no effective medicine for FGR. Phosphodiesterase-5 (PDE-5) inhibitors have been shown in pre-clinical studies to improve FGR. This study aimed to evaluate the latest evidence about the clinical outcomes and safety of PDE-5 inhibitors for the management of FGR.Methods: Eight databases (PubMed, Embase, Medline, Web of Science, Cochrane Library, Chinese National Knowledge Infrastructure, Chinese Biomedical Database and WangFang Database) were searched for English and Chinese articles published from the database inception to December 2023. Randomized controlled trials (RCTs) reporting the use of PDE-5 inhibitors in FGR were included. The quality of the RCTs was assessed using the Cochrane Risk of Bias Tool. Odds ratio and mean difference (MD) (95% confidence intervals) were pooled for meta-analysis.Results: From 253 retrieved publications, 16 studies involving 1,492 pregnant women met the inclusion criteria. Only sildenafil (15 RCTs) and tadalafil (1 RCT) were studied for FGR. Compared with the control group (placebo, no treatment, or other medication therapies), sildenafil increased birth weight, pregnancy prolongation and umbilical artery pulsatility indices. However, it also increased the risk of pulmonary hypertension in newborns, as well as headache and flushing/rash in mothers. There were no significant differences in gestation age, perinatal mortality or major neonatal morbidity, stillbirth, neonate death, infants admitted to neonatal intensive care unit, intraventricular hemorrhage and necrotizing enterocolitis in infants, as well as pregnancy hypertension and gastrointestinal side effects in mothers between the treatment and the control groups.Discussion: Sildenafil was the most investigated PDE-5 inhibitors for FGR. Current evidence suggests that sildenafil can improve birth weight and duration of pregnancy but at the same time increase the risk of neonatal pulmonary hypertension. It remains uncertain whether the benefits of sildenafil in FGR outweigh the risks and further high-quality RCTs are warranted.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=325909

AIM:This article aims to quantitatively analyze the growth trend of listed pharmaceutical companies in the US and China by a machine learning algorithm. BACKGROUND:In the last two decades, the global pharmaceutical industry has faced the dilemma of low research & development (R&D) success rate. The US is the world's largest pharmaceutical market, while China is the largest emerging market. OBJECTIVE:To collect data from the database and apply machine learning to build the model. METHODS:LightGBM algorithm was used to build the model and identify the factor important to the performance of pharmaceutical companies. RESULTS:The prediction accuracy for US companies was 80.3%, while it was 64.9% for Chinese companies. The feature importance shows that the net profit growth rate and debt liability ratio are significant in financial indicators. The results indicated that the US may continue to dominate the global pharmaceutical industry, while several Chinese pharmaceutical companies rose sharply after 2015 with the narrowing gap between the Chinese and US pharmaceutical industries. CONCLUSION:In summary, our research quantitatively analyzed the growth trend of listed pharmaceutical companies in the US and China by a machine learning algorithm, which provide a novel perspective for the global pharmaceutical industry. According to the R&D capability and profitability, 141 US-listed and 129 China-listed pharmaceutical companies were divided into four levels to evaluate the growth trend of pharmaceutical firms.

Ternary cyclodextrin (CD) complexes (drug/CD/polymer) can effectively improve the solubility of water-insoluble drugs with large size than binary CD formulations. However, ternary formulations are screened by a trial-and-error approach, which is laborious and material-wasting. Current research aims to develop a prediction model for ternary CD formulations by combined machine learning and molecular modeling. 596 ternary formulations data were collected to build a prediction model by machine learning. The random forest model achieved good performance with R2 = 0.887 in ST prediction and R2 = 0.815 in ST/SB prediction. Two ternary formulations (Hydrocortisone/β-CD/HPMC and dovitinib/γ-CD/CMC) were used to validate the prediction model. Molecular modeling results showed that HPMC not only warped around hydrocortisone but also prevented CD molecules from self-aggregation to increase solubility. In conclusion, a prediction model for the ternary CD formulations was successfully developed, which will significantly accelerate the formulation screening process to benefit the formulation development of water-insoluble drugs.

Matrix metalloproteinases (MMPs) are involved in the cleavage of several components of the extracellular matrix and serve important roles in tumor growth, metastasis and invasion. Previous studies have focused on the expression of one or several MMPs in esophageal squamous cell carcinoma (ESCC); however, in the present study, the transcriptomics of all 23 MMPs were systematically investigated with a focus on the prognostic value of the combination of MMPs. In this study, 8 overlapping differentially expressed genes of the MMP family were identified based on data obtained from Gene Expression Omnibus and The Cancer Genome Atlas. The prognostic value of these MMPs were investigated; the receiver operating characteristic curves, survival curves and nomograms showed that the combination of 6 selected MMPs possessed a good predictive ability, which was more accurate than the prediction model based on Tumor-Node-Metastasis stage. Gene set enrichment analysis and gene co-expression analysis were performed to investigate the potential mechanism of action of MMPs in ESCC. The MMP family was associated with several signaling pathways, such as epithelial-mesenchymal transition (EMT), Notch, TGF-beta, mTOR and P53. Cell Counting Kit-8, colony formation, wound healing assays and western blotting were used to determine the effect of BB-94, a pan-MMP inhibitor, on proliferation and migration of ESCC cells. BB-94 treatment decreased ESCC cell growth, migration and EMT. Therefore, MMPs may serve both as diagnostic and prognostic biomarkers of ESCC, and MMP inhibition may be a promising preventive and therapeutic strategy for patients with ESCC.

Background: Esophageal squamous cell carcinoma (ESCC) is a subtype of esophageal cancer with high incidence and mortality.Due to the poor five-year survival rates of patients with ESCC, exploring novel diagnostic markers for early ESCC is emergent.Collagen, the abundant constituent of extracellular matrix, plays a critical role in tumor growth and epithelial-mesenchymal transition.However, the clinical significance of collagen genes in ESCC has been rarely studied.In this work, we systematically analyzed the gene expression of whole collagen family in ESCC, aiming to search for ideal biomarkers.Methods: Clinical data and gene expression profiles of ESCC patients were collected from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases.Bioinformatics methods, including differential expression analysis, survival analysis, gene sets enrichment analysis (GSEA) and coexpression network analysis, were performed to investigate the correlation between the expression patterns of 44 collagen family genes and the development of ESCC.Results: 22 genes of collagen family were identified as differentially expressed genes (DEGs) in both the two datasets.Among them, COL1A1, COL10A1 and COL11A1 were particularly up-regulated in ESCC tissues compared to normal controls, while COL4A4, COL6A5 and COL14A1 were notably down-regulated.Besides, patients with low COL6A5 expression or high COL18A1 expression showed poor survival.In addition, a 7-gene prediction model was established based on collagen gene expression to predict patient survival, which had better predictive accuracy than the tumor-node-metastasis (TNM) staging based model.Finally, GSEA results suggested that collagen genes might be tightly associated with PI3K/Akt/mTOR pathway, p53 pathway, apoptosis, cell cycle, etc. Conclusion:Several collagen genes could be potential diagnostic and prognostic biomarkers for ESCC.Moreover, a novel 7-gene prediction model is probably useful for predicting survival outcomes of ESCC patients.These findings may facilitate early detection of ESCC and help improves prognosis of the patients.

Emetine, an amoebicidal drug, exerts potent anticancer activity against various solid tumors, however, the underlying molecular mechanism remains unclear. In the present study, the effects of emetine were investigated on various proteins involved in the Wnt/beta-catenin signaling pathway, which has been linked to various human cancers. It was revealed that emetine blocked Wnt/beta-catenin signaling by targeting components of this pathway, including the low-density lipoprotein-receptor-related protein 6 (LRP6) and disheveled (DVL). Moreover, nanomolar concentrations of emetine decreased phosphorylation of these proteins and suppressed the expression of Wnt target genes, including fibronectin, frizzled-7 (Fzd7), c-Myc, Nanog and CD133 in MDA-MB-231 and MDA-MB-468 breast cancer cells. Additionally, emetine treatment induced apoptosis and suppressed the viability, migration, invasion, and sphere formation of breast cancer cells. Collectively the present results indicated that emetine antagonizes Wnt/beta-catenin signaling, providing insight into the molecular mechanism underlying the anticancer activity of emetine.

Background: Esophageal squamous cell carcinoma (ESCC) is a subtype of esophageal cancer with high incidence and mortality. Due to the poor 5-year survival rates of patients with ESCC, exploring novel diagnostic markers for early ESCC is emergent. Collagen, the abundant constituent of extracellular matrix, plays a critical role in tumor growth and epithelial-mesenchymal transition. However, the clinical significance of collagen genes in ESCC has been rarely studied. In this work, we systematically analyzed the gene expression of whole collagen family in ESCC, aiming to search for ideal biomarkers. Methods: Clinical data and gene expression profiles of ESCC patients were collected from The Cancer Genome Atlas and the gene expression omnibus databases. Bioinformatics methods, including differential expression analysis, survival analysis, gene sets enrichment analysis (GSEA) and co-expression network analysis, were performed to investigate the correlation between the expression patterns of 44 collagen family genes and the development of ESCC. Results: A total of 22 genes of collagen family were identified as differentially expressed genes in both the two datasets. Among them, COL1A1, COL10A1 and COL11A1 were particularly up-regulated in ESCC tissues compared to normal controls, while COL4A4, COL6A5 and COL14A1 were notably down-regulated. Besides, patients with low COL6A5 expression or high COL18A1 expression showed poor survival. In addition, a 7-gene prediction model was established based on collagen gene expression to predict patient survival, which had better predictive accuracy than the tumor-node-metastasis staging based model. Finally, GSEA results suggested that collagen genes might be tightly associated with PI3K/Akt/mTOR pathway, p53 pathway, apoptosis, cell cycle, etc. Conclusion: Several collagen genes could be potential diagnostic and prognostic biomarkers for ESCC. Moreover, a novel 7-gene prediction model is probably useful for predicting survival outcomes of ESCC patients. These findings may facilitate early detection of ESCC and help improves prognosis of the patients.

Background Homoharringtonine (HHT) is a natural alkaloid with potent antitumor activity, but its precise mechanism of action is still poorly understood. Methods We examined the effect of HHT on alternative splicing of Bcl-x and Caspase 9 in various cells using semi-quantitative reverse transcriptase-polymerase chain reaction (RT-PCR). The mechanism of HHT-affected alternative splicing in these cells was investigated by treatment with protein phosphatase inhibitors and overexpression of a protein phosphatase. Results Treatment with HHT downregulated the levels of anti-apoptotic Bcl-xL and Caspase 9b mRNA with a concomitant increase in the mRNA levels of pro-apoptotic Bcl-xS and Caspase 9a in a dose- and time-dependent manner. Calyculin A, an inhibitor of protein phosphatase 1 (PP1) and protein phosphatase 2A (PP2A), significantly inhibited the effects of HHT on the alternative splicing of Bcl-x and Caspase 9, in contrast to okadaic acid, a specific inhibitor of PP2A. Overexpression of PP1 resulted in a decrease in the ratio of Bcl-xL/xS and an increase in the ratio of Caspase 9a/9b. Moreover, the effects of HHT on Bcl-x and Caspase 9 splicing were enhanced in response to PP1 overexpression. These results suggest that HHT-induced alternative splicing of Bcl-x and Caspase 9 is dependent on PP1 activation. In addition, overexpression of PP1 could induce apoptosis and sensitize MCF7 cells to apoptosis induced by HHT. Conclusion Homoharringtonine regulates the alternative splicing of Bcl-x and Caspase 9 through a PP1-dependent mechanism. Our study reveals a novel mechanism underlying the antitumor activities of HHT.