Artificial Intelligence-Driven Platform: Unveiling Critical Hepatic Molecular Alterations in Hepatocellular Carcinoma Development.

Since most Hepatocellular Carcinoma (HCC) typically arises as a consequence of long-term liver damage, the hepatic molecular characteristics are closely related to the occurrence of HCC. Gaining comprehensive information about the location, morphology, and hepatic molecular alterations related to HCC is essential for accurate diagnosis. However, there is a dearth of technological advancements capable of concurrently providing precise HCC diagnosis and discerning the accompanying hepatic molecular alterations. In this study, an integrated information system is developed for the pathological-level diagnosis of HCC and the revelation of critical molecular alterations in the liver. This system utilizes computed tomography/Surface-enhanced Raman scattering combined with an artificial intelligence strategy to establish connections between the occurrence of HCC and alterations in hepatic biomolecules. Employing artificial intelligence techniques, the SERS spectra from both healthy and HCC groups are successfully classified into two distinct categories with a remarkable accuracy rate of 91.38%. Based on molecular profiling, it is identified that the nucleotide-to-lipid signal ratio holds significant potential as a reliable indicator for the occurrence of HCC, thereby serving as a promising tool for prevention and therapeutic surveillance.

FKBP11 improves the malignant property of osteosarcoma cells and acts as a prognostic factor of osteosarcoma.

BACKGROUND: Osteosarcoma has become the most common bone malignancy in adolescents. Although the clinical treatment of osteosarcoma has advanced considerably in recent years, the 5-year survival rate has not improved significantly. Recently, many studies have shown that mRNA has unique advantages as a target for drug therapy. Therefore, this study aimed to identify a new prognostic factor and provide a new target for the treatment of osteosarcoma to improve the prognosis of patients. METHODS AND RESULTS: We selected prognostic genes that are closely associated with osteosarcoma clinical features by obtaining osteosarcoma patient information from the GTEx and TARGET databases, and then we developed a risk model. We detected the expression of FKBP11 in osteosarcoma by qRT-PCR, western blotting, and immunohistochemistry and performed CCK-8, Transwell, colony formation, and flow cytometry assays to reveal the regulatory role of FKBP11. We found that FKBP11 was highly expressed in osteosarcoma; silencing FKBP11 expression suppressed the invasion and migration of osteosarcoma cells, slowed cell proliferation, and promoted apoptosis. We also found that silencing the expression of FKBP11 led to inhibition of MEK/ERK phosphorylation. CONCLUSIONS: In conclusion, we validated that the prognostic factor FKBP11 is closely associated with osteosarcoma. Additionally, we identified a novel mechanism by which FKBP11 ameliorates the malignant properties of osteosarcoma cells through the MAPK pathway and serves as a prognostic factor in osteosarcoma. This study provides a new method for the treatment of osteosarcoma.

Routine chest CT combined with the osteoporosis self-assessment tool for Asians (OSTA): a screening tool for patients with osteoporosis.

INTRODUCTION: The osteoporosis self-assessment tool for Asians (OSTA) is a common screening tool for osteoporosis. The seventh thoracic CT (CT-T7) Hounsfield unit (HU) measured by chest CT correlates with osteoporosis. This study aimed to investigate the diagnostic value of OSTA alone, CT-T7 alone, or the combination of OSTA and CT-T7 in osteoporosis. MATERIALS AND METHODS: In this study, 1268 participants were grouped into 586 men and 682 women. We established multiple linear regression models by combining CT-T7 and OSTA. Receiver operating characteristic (ROC) curves were used to evaluate the ability to diagnose osteoporosis. RESULTS: In the male group, the mean age was 59.02 years, and 108 patients (18.4%) had osteoporosis. In the female group, the mean age was 63.23 years, and 308 patients (45.2%) had osteoporosis. By ROC curve comparison, the CT-T7 (male, AUC = 0.789, 95% CI 0.745-0.832; female, AUC = 0.835, 95% CI 0.805-0.864) in the diagnosis of osteoporosis was greater than the OSTA (male, AUC = 0.673, 95% CI 0.620-0.726; female, AUC = 0.775, 95% CI 0.741-0.810) in both the male and female groups (p < 0.001). When OSTA was combined with CT, the equation of multiple linear regression (MLR) was obtained as follows: female = 3.020-0.028*OSTA-0.004*CT-T7. In the female group, it was found that the AUC of MLR (AUC = 0.853, 95% CI 0.825-0.880) in the diagnosis of osteoporosis was larger than that of CT-T7 (p < 0.01). When the MLR was 2.65, the sensitivity and specificity were 53.9% and 90%, respectively. CONCLUSION: For a patient who has completed chest CT, CT-T7 (HU) combined with OSTA is recommended to identify the high-risk population of osteoporosis, and it has a higher diagnostic value than OSTA alone or CT-T7 alone, especially among females. For a female with MLR greater than 2.65, further DXA examination is needed.