Clinical Study Intercalary Reconstruction after Wide Resection of Malignant Bone Tumors of the Lower Extremity Using a Composite Graft with a Devitalized Autograft and a Vascularized Fibula

Introduction. Although several intercalary reconstructions after resection of a lower extremity malignant bone tumor are reported, there are no optimal methods which can provide a long-term reconstruction with fewest complications. We present the outcome of reconstruction using a devitalized autograft and a vascularized fibula graft composite. Materials and Methods. We conducted a retrospective review of 11 patients (7 males, 4 females; median age 27 years) undergoing reconstruction using a devitalized autograft (pasteurization ( = 6), deep freezing ( = 5)) and a vascularized fibula graft composite for lower extremity malignant bone tumors (femur ( = 10), tibia ( = 1)). Results. The mean period required for callus formation and bone union was 4.4 months and 9.9 months, respectively. Four postoperative complications occurred in 3 patients: 2 infections (1 pasteurized autograft, 1 frozen autograft) and 1 fracture and 1 implant failure (both in pasteurized autografts). Graft removal was required in 2 patients with infections. The mean MSTS score was 81% at last follow-up. Conclusions. Although some complications were noted in early cases involving a pasteurized autograft, our novel method involving a combination of a frozen autograft with a vascularized fibula graft and rigid fixation with a locking plate may offer better outcomes than previously reported allografts or devitalized autografts

Predicting reliable H2_2 column density maps from molecular line data using machine learning

The total mass estimate of molecular clouds suffers from the uncertainty in the H$_2$-CO conversion factor, the so-called $X_{\rm CO}$ factor, which is used to convert the $^{12}$CO (1--0) integrated intensity to the H$_2$ column density. We demonstrate the machine learning's ability to predict the H$_2$ column density from the $^{12}$CO, $^{13}$CO, and C$^{18}$O (1--0) data set of four star-forming molecular clouds; Orion A, Orion B, Aquila, and M17. When the training is performed on a subset of each cloud, the overall distribution of the predicted column density is consistent with that of the Herschel column density. The total column density predicted and observed is consistent within 10\%, suggesting that the machine learning prediction provides a reasonable total mass estimate of each cloud. However, the distribution of the column density for values $> \sim 2 \times 10^{22}$ cm$^{-2}$, which corresponds to the dense gas, could not be predicted well. This indicates that molecular line observations tracing the dense gas are required for the training. We also found a significant difference between the predicted and observed column density when we created the model after training the data on different clouds. This highlights the presence of different $X_{\rm CO}$ factors between the clouds, and further training in various clouds is required to correct for these variations. We also demonstrated that this method could predict the column density toward the area not observed by Herschel if the molecular line and column density maps are available for the small portion, and the molecular line data are available for the larger areas.Comment: Accepted for publication in MNRA

Distance determination of molecular clouds in the 1st quadrant of the Galactic plane using deep learning : I. Method and Results

Machine learning has been successfully applied in varied field but whether it is a viable tool for determining the distance to molecular clouds in the Galaxy is an open question. In the Galaxy, the kinematic distance is commonly employed as the distance to a molecular cloud. However, there is a problem in that for the inner Galaxy, two different solutions, the ``Near'' solution, and the ``Far'' solution, can be derived simultaneously. We attempted to construct a two-class (``Near'' or ``Far'') inference model using a Convolutional Neural Network (CNN), a form of deep learning that can capture spatial features generally. In this study, we used the CO dataset toward the 1st quadrant of the Galactic plane obtained with the Nobeyama 45-m radio telescope (l = 62-10 degree, |b| < 1 degree). In the model, we applied the three-dimensional distribution (position-position-velocity) of the 12CO (J=1-0) emissions as the main input. The dataset with ``Near'' or ``Far'' annotation was made from the HII region catalog of the infrared astronomy satellite WISE to train the model. As a result, we could construct a CNN model with a 76% accuracy rate on the training dataset. By using the model, we determined the distance to molecular clouds identified by the CLUMPFIND algorithm. We found that the mass of the molecular clouds with a distance of < 8.15 kpc identified in the 12CO data follows a power-law distribution with an index of about -2.3 in the mass range of M >10^3 Msun. Also, the detailed molecular gas distribution of the Galaxy as seen from the Galactic North pole was determined.Comment: 29 pages, 12 figure

Late Arterial Thrombosis after Microvascular Head and Neck Reconstruction due to Combined Factors of Pedicle Artery Loop and Submandibular Gland Swelling

Summary:. Late arterial thrombosis of a free flap is rare and usually unsalvageable because it is hard to detect. We herein report 2 cases of arterial thrombosis of a free flap after microvascular head and neck reconstruction due to the combined factors of pedicle artery loop and compression by a swollen submandibular gland, the occurrence of thrombosis in both of which was > 72 hours after the operation. In case 1, the arterial thrombosis was undetectable, and it was too late for a successful take-back operation, so the flap was lost. However, we applied the lessons learned from case 1 and were able to detect the late arterial thrombosis of case 2 at an early stage; we subsequently salvaged the flap successfully. During the take-back operation in both cases, it was found that the submandibular gland became swollen and compressed the pedicle artery, which then became occluded due to a steep loop formation. Postoperative swelling of the submandibular gland can sometimes compress the vascular pedicle, and complete occlusion of the pedicle artery may occur when it is looped. Meticulous care concerning the geometry of the vascular pedicle is required to avoid such complications