Purpose: To evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT). Materials and Methods: This retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CACscoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients’ medical records were monitored until November 2023. Results: A total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers’ sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all p<0.001), without a notable increase in the number of false-positives per case (0.11 vs. 0.13, p=0.078 for reader 1; 0.11 vs. 0.11, p>0.999 for reader 2). Per-patient analysis also enhanced sensitivity with DL-CAD assistance (73% vs. 84%, p<0.001 for reader 1; 89% vs. 91%, p=0.250 for reader 2). During follow-up, lung cancer was diagnosed in four patients (1.5%). Among them, two had lesions detected on CAC-scoring CT, both of which were successfully identified by DL-CAD. Conclusion DL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CACscoring CT scans, improving detection sensitivity without significantly increasing false-positives.

Anticancer activities of Licochalcone D (LCD) in human colorectal cancer (CRC) cells HCT116 and oxaliplatin-resistant HCT116 (HCT116-OxR) were determined. Cell viability assay and soft agar assay were used to analyze antiproliferative activity of LCD.Flow cytometry was performed to determine effects of LCD on apoptosis, cell cycle distribution, reactive oxygen species (ROS), mitochondrial membrane potential (MMP) dysfunction, and multi-caspase activity in CRC cells. Western blot analysis was used to monitor levels of proteins involved in cell cycle and apoptosis signaling pathways. LCD suppressed the growth and anchorageindependent colony formation of both HCT116 and HCT116-OxR cells. Cell cycle analysis by flow cytometry indicated that LCD induced cell cycle arrest and increased cells in sub-G1 phase. In parallel with the antiproliferative effect of LCD, LCD up-regulated levels of p21 and p27 while downregulating cyclin B1 and cdc2. In addition, phosphorylation levels of JNK and p38 mitogen-activated protein kinase (MAPK) were increased by LCD. Inhibition of these kinases somehow prevented the antiproliferative effect of LCD. Moreover, LCD increased ROS and deregulated mitochondrial membrane potential, leading to the activation of multiple caspases. An ROS scavenger N-acetyl-cysteine (NAC) or pan-caspase inhibitor Z-VAD-FMK prevented the antiproliferative effect of LCD, supporting that ROS generation and caspase activation mediated LCD-induced apoptosis in CRC cells. In conclusion, LCD exerted antitumor activity in CRC cells by inducing ROS generation and phosphorylation of JNK and p38 MAPK. These results support that LCD could be further developed as a chemotherapeutic agent for treating CRC.

Anticancer activities of Licochalcone D (LCD) in human colorectal cancer (CRC) cells HCT116 and oxaliplatin-resistant HCT116 (HCT116-OxR) were determined. Cell viability assay and soft agar assay were used to analyze antiproliferative activity of LCD.Flow cytometry was performed to determine effects of LCD on apoptosis, cell cycle distribution, reactive oxygen species (ROS), mitochondrial membrane potential (MMP) dysfunction, and multi-caspase activity in CRC cells. Western blot analysis was used to monitor levels of proteins involved in cell cycle and apoptosis signaling pathways. LCD suppressed the growth and anchorageindependent colony formation of both HCT116 and HCT116-OxR cells. Cell cycle analysis by flow cytometry indicated that LCD induced cell cycle arrest and increased cells in sub-G1 phase. In parallel with the antiproliferative effect of LCD, LCD up-regulated levels of p21 and p27 while downregulating cyclin B1 and cdc2. In addition, phosphorylation levels of JNK and p38 mitogen-activated protein kinase (MAPK) were increased by LCD. Inhibition of these kinases somehow prevented the antiproliferative effect of LCD. Moreover, LCD increased ROS and deregulated mitochondrial membrane potential, leading to the activation of multiple caspases. An ROS scavenger N-acetyl-cysteine (NAC) or pan-caspase inhibitor Z-VAD-FMK prevented the antiproliferative effect of LCD, supporting that ROS generation and caspase activation mediated LCD-induced apoptosis in CRC cells. In conclusion, LCD exerted antitumor activity in CRC cells by inducing ROS generation and phosphorylation of JNK and p38 MAPK. These results support that LCD could be further developed as a chemotherapeutic agent for treating CRC.

Purpose: To evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT). Materials and Methods: This retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CACscoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients’ medical records were monitored until November 2023. Results: A total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers’ sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all p<0.001), without a notable increase in the number of false-positives per case (0.11 vs. 0.13, p=0.078 for reader 1; 0.11 vs. 0.11, p>0.999 for reader 2). Per-patient analysis also enhanced sensitivity with DL-CAD assistance (73% vs. 84%, p<0.001 for reader 1; 89% vs. 91%, p=0.250 for reader 2). During follow-up, lung cancer was diagnosed in four patients (1.5%). Among them, two had lesions detected on CAC-scoring CT, both of which were successfully identified by DL-CAD. Conclusion DL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CACscoring CT scans, improving detection sensitivity without significantly increasing false-positives.

Purpose: To evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT). Materials and Methods: This retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CACscoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients’ medical records were monitored until November 2023. Results: A total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers’ sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all p<0.001), without a notable increase in the number of false-positives per case (0.11 vs. 0.13, p=0.078 for reader 1; 0.11 vs. 0.11, p>0.999 for reader 2). Per-patient analysis also enhanced sensitivity with DL-CAD assistance (73% vs. 84%, p<0.001 for reader 1; 89% vs. 91%, p=0.250 for reader 2). During follow-up, lung cancer was diagnosed in four patients (1.5%). Among them, two had lesions detected on CAC-scoring CT, both of which were successfully identified by DL-CAD. Conclusion DL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CACscoring CT scans, improving detection sensitivity without significantly increasing false-positives.

Purpose: To evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT). Materials and Methods: This retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CACscoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients’ medical records were monitored until November 2023. Results: A total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers’ sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all p<0.001), without a notable increase in the number of false-positives per case (0.11 vs. 0.13, p=0.078 for reader 1; 0.11 vs. 0.11, p>0.999 for reader 2). Per-patient analysis also enhanced sensitivity with DL-CAD assistance (73% vs. 84%, p<0.001 for reader 1; 89% vs. 91%, p=0.250 for reader 2). During follow-up, lung cancer was diagnosed in four patients (1.5%). Among them, two had lesions detected on CAC-scoring CT, both of which were successfully identified by DL-CAD. Conclusion DL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CACscoring CT scans, improving detection sensitivity without significantly increasing false-positives.

Anticancer activities of Licochalcone D (LCD) in human colorectal cancer (CRC) cells HCT116 and oxaliplatin-resistant HCT116 (HCT116-OxR) were determined. Cell viability assay and soft agar assay were used to analyze antiproliferative activity of LCD.Flow cytometry was performed to determine effects of LCD on apoptosis, cell cycle distribution, reactive oxygen species (ROS), mitochondrial membrane potential (MMP) dysfunction, and multi-caspase activity in CRC cells. Western blot analysis was used to monitor levels of proteins involved in cell cycle and apoptosis signaling pathways. LCD suppressed the growth and anchorageindependent colony formation of both HCT116 and HCT116-OxR cells. Cell cycle analysis by flow cytometry indicated that LCD induced cell cycle arrest and increased cells in sub-G1 phase. In parallel with the antiproliferative effect of LCD, LCD up-regulated levels of p21 and p27 while downregulating cyclin B1 and cdc2. In addition, phosphorylation levels of JNK and p38 mitogen-activated protein kinase (MAPK) were increased by LCD. Inhibition of these kinases somehow prevented the antiproliferative effect of LCD. Moreover, LCD increased ROS and deregulated mitochondrial membrane potential, leading to the activation of multiple caspases. An ROS scavenger N-acetyl-cysteine (NAC) or pan-caspase inhibitor Z-VAD-FMK prevented the antiproliferative effect of LCD, supporting that ROS generation and caspase activation mediated LCD-induced apoptosis in CRC cells. In conclusion, LCD exerted antitumor activity in CRC cells by inducing ROS generation and phosphorylation of JNK and p38 MAPK. These results support that LCD could be further developed as a chemotherapeutic agent for treating CRC.