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Abstract To efficiently allocate medical resources at disaster sites, medical workers perform triage to prioritize medical treatments based on the severity of the wounded or sick. In such instances, evaluators often assess the severity status of the wounded or sick quickly, but their measurements are qualitative and rely on experience. Therefore, we developed a wearable device called Medic Hand in this study to extend the functionality of a medical worker’s hand so as to measure multiple biometric indicators simultaneously without increasing the number of medical devices to be carried. Medic Hand was developed to quantitatively and efficiently evaluate "perfusion" during triage. Speed is essential during triage at disaster sites, where time and effort are often spared to attach medical devices to patients, so the use of Medic Hand as a biometric measurement device is more efficient for collecting biometric information. For Medic Hand to be handy during disasters, it is essential to understand and improve upon factors that facilitate its public acceptance. To this end, this paper reports on the usability evaluation of Medic Hand through a questionnaire survey of nonmedical workers.

Abstract Objectives To investigate the relationship between low kidney volume and subsequent estimated glomerular filtration rate (eGFR) decline in eGFR category G2 (60–89 mL/min/1.73 m2) population. Methods In this retrospective study, we evaluated 5531 individuals with eGFR category G2 who underwent medical checkups at our institution between November 2006 and October 2017. Exclusion criteria were absent for follow-up visit, missing data, prior renal surgery, current renal disease under treatment, large renal masses, and horseshoe kidney. We developed a 3D U-net-based automated system for renal volumetry on CT images. Participants were grouped by sex-specific kidney volume deviations set at mean minus one standard deviation. After 1:1 propensity score matching, we obtained 397 pairs of individuals in the low kidney volume (LKV) and control groups. The primary endpoint was progression of eGFR categories within 5 years, assessed using Cox regression analysis. Results This study included 3220 individuals (mean age, 60.0 ± 9.7 years; men, n = 2209). The kidney volume was 404.6 ± 67.1 and 376.8 ± 68.0 cm3 in men and women, respectively. The low kidney volume (LKV) cutoff was 337.5 and 308.8 cm3 for men and women, respectively. LKV was a significant risk factor for the endpoint with an adjusted hazard ratio of 1.64 (95% confidence interval: 1.09–2.45; p = 0.02). Conclusion Low kidney volume may adversely affect subsequent eGFR maintenance; hence, the use of imaging metrics may help predict eGFR decline. Critical relevance statement Low kidney volume is a significant predictor of reduced kidney function over time; thus, kidney volume measurements could aid in early identification of individuals at risk for declining kidney health. Key points • This study explores how kidney volume affects subsequent kidney function maintenance. • Low kidney volume was associated with estimated glomerular filtration rate decreases. • Low kidney volume is a prognostic indicator of estimated glomerular filtration rate decline. Graphical Abstract

Introduction: Adenosquamous carcinoma (ASC) of the ampulla of Vater (AmV) is rare. The prognosis is generally worse in patients undergoing resection of ASC of the AmV than in those undergoing resection of adenocarcinoma of the AmV because the former shows early recurrence after surgery. A treatment strategy for ASC of the AmV has not been established, and the efficacy of adjuvant chemotherapy after curative resection is unclear. Given the paucity of data, we report a case of ASC of the AmV that was curatively resected and treated with adjuvant chemotherapy. Case Presentation: A 66-year-old man presented with pruritus and anorexia. Contrast-enhanced computed tomography revealed a tumor measuring 1.6 cm in diameter located at the AmV and distal bile duct. Biopsy revealed adenocarcinoma of the AmV. The patient underwent subtotal stomach-preserving pancreaticoduodenectomy. Histopathological examination contradictorily revealed ASC of the AmV and lymph node metastases. The postoperative course of the patient was uneventful, and he was discharged on day 25. The patient underwent S-1 adjuvant chemotherapy for 6 months and did not exhibit any postoperative recurrence for a follow-up duration of 28 months. Conclusion: Although treatment strategy for ASC of the AmV has not been established, our case shows that surgery followed by S-1 adjuvant chemotherapy could improve prognosis of patients with such tumors. However, further research is required to determine the efficacy of adjuvant chemotherapy and treatment strategies for resectable ASC of the AmV.

BackgroundPrevious research applying large language models (LLMs) to medicine was focused on text-based information. Recently, multimodal variants of LLMs acquired the capability of recognizing images. ObjectiveWe aim to evaluate the image recognition capability of generative pretrained transformer (GPT)-4V, a recent multimodal LLM developed by OpenAI, in the medical field by testing how visual information affects its performance to answer questions in the 117th Japanese National Medical Licensing Examination. MethodsWe focused on 108 questions that had 1 or more images as part of a question and presented GPT-4V with the same questions under two conditions: (1) with both the question text and associated images and (2) with the question text only. We then compared the difference in accuracy between the 2 conditions using the exact McNemar test. ResultsAmong the 108 questions with images, GPT-4V’s accuracy was 68% (73/108) when presented with images and 72% (78/108) when presented without images (P=.36). For the 2 question categories, clinical and general, the accuracies with and those without images were 71% (70/98) versus 78% (76/98; P=.21) and 30% (3/10) versus 20% (2/10; P≥.99), respectively. ConclusionsThe additional information from the images did not significantly improve the performance of GPT-4V in the Japanese National Medical Licensing Examination.

Abstract Lower extremity lymphedema (LEL) is a common complication after gynecological cancer treatment, which significantly reduces the quality of life. While early diagnosis and intervention can prevent severe complications, there is currently no consensus on the optimal screening strategy for postoperative LEL. In this study, we developed a computer-aided diagnosis (CAD) software for LEL screening in pelvic computed tomography (CT) images using deep learning. A total of 431 pelvic CT scans from 154 gynecological cancer patients were used for this study. We employed ResNet-18, ResNet-34, and ResNet-50 models as the convolutional neural network (CNN) architecture. The input image for the CNN model used a single CT image at the greater trochanter level. Fat-enhanced images were created and used as input to improve classification performance. Receiver operating characteristic analysis was used to evaluate our method. The ResNet-34 model with fat-enhanced images achieved the highest area under the curve of 0.967 and an accuracy of 92.9%. Our CAD software enables LEL diagnosis from a single CT image, demonstrating the feasibility of LEL screening only on CT images after gynecologic cancer treatment. To increase the usefulness of our CAD software, we plan to validate it using external datasets.

Local differential privacy algorithms combined with deep generative models can enhance secure medical image sharing among researchers in the public domain without central administrators; however, these images were limited to the generation of low-resolution images, which are very insufficient for diagnosis by medical doctors. To enhance the performance of deep generative models so that they can generate high-resolution medical images, we propose a large-scale diffusion model that can, for the first time, unconditionally generate high-resolution (256×256×256) volumetric medical images (head magnetic resonance images). This diffusion model has 19 billion parameters, but to make it easy to train it, we temporally divided the model into 200 submodels, each of which has 95 million parameters. Moreover, on the basis of this new diffusion model, we propose another formulation of image anonymization with which the processed images can satisfy provable Gaussian local differential privacy and with which we can generate images semantically different from the original image but belonging to the same class. We believe that the formulation of this new diffusion model and the implementation of local differential privacy algorithms combined with the diffusion models can contribute to the secure sharing of practical images upstream of data processing.

An educational augmented reality auscultation system (EARS) is proposed to enhance the reality of auscultation training using a simulated patient. The conventional EARS cannot accurately reproduce breath sounds according to the breathing of a simulated patient because the system instructs the breathing rhythm. In this study, we propose breath measurement methods that can be integrated into the chest piece of a stethoscope. We investigate methods using the thoracic variations and frequency characteristics of breath sounds. An accelerometer, a magnetic sensor, a gyro sensor, a pressure sensor, and a microphone were selected as the sensors. For measurement with the magnetic sensor, we proposed a method by detecting the breathing waveform in terms of changes in the magnetic field accompanying the surface deformation of the stethoscope based on thoracic variations using a magnet. During breath sound measurement, the frequency spectra of the breath sounds acquired by the built-in microphone were calculated. The breathing waveforms were obtained from the difference in characteristics between the breath sounds during exhalation and inhalation. The result showed the average value of the correlation coefficient with the reference value reached 0.45, indicating the effectiveness of this method as a breath measurement method. And the evaluations suggest more accurate breathing waveforms can be obtained by selecting the measurement method according to breathing method and measurement point.

Objective: This study aimed to determine a standardized cut-off value for abnormal 18F-fluorodeoxyglucose (FDG) accumulation in the thyroid gland. Methods: Herein, 7013 FDG–PET/CT scans were included. An automatic thy roid segmentation method using two U-nets (2D- and 3D-U-net) was con structed; mean FDG standardized uptake value (SUV), CT value, and volume of th e thyroid gland were obtained from each participant. The values were categorized by thyroid function into three groups based on serum thyroid-stimulating hormone levels. Thyroid function and mean SUV with increments of 1 were analyzed, and risk for thyro id dysfunction was calculated. Thyroid dysfunction detection ability was examined using a machine learning method (LightGBM, Microsoft) with age, sex, height, weight, CT value, volume, and mean SUV as explanatory variables. Results: Mean SUV was significantly higher in females with hypothyroidi sm. Almost 98.9% of participants in the normal group had mean SUV < 2 and 93.8% participants with mean SUV < 2 had normal thyroid function. The hypothyroidism group h ad more cases with mean SUV ≥ 2. The relative risk of having abnormal thyroid function was 4 .6 with mean SUV ≥ 2. The sensitivity and specificity for detecting thyroid dysfun ction using LightGBM (Microsoft) were 14.5 and 99%, respectively. Conclusions: Mean SUV ≥ 2 was strongly associated with abnormal thyroid function in this large cohort, indicating that mean SUV with FDG–PET/CT can be used as a criterion for thyroid evaluation. Preliminarily, this study shows the pot ential utility of detecting thyroid dysfunction based on imaging findings.

Ultra-sparse-view computed tomography (CT) algorithms can reduce radiation exposure for patients, but these algorithms lack an explicit cycle consistency loss minimization and an explicit log-likelihood maximization in testing. Here, we propose X2CT-FLOW for the maximum a posteriori (MAP) reconstruction of a three-dimensional (3D) chest CT image from a single or a few two-dimensional (2D) projection images using a progressive flow-based deep generative model, especially for ultra-low-dose protocols. The MAP reconstruction can simultaneously optimize the cycle consistency loss and the log-likelihood. We applied X2CT-FLOW for the reconstruction of 3D chest CT images from biplanar projection images without noise contamination (assuming a standard-dose protocol) and with strong noise contamination (assuming an ultra-low-dose protocol). We simulated an ultra-low-dose protocol. With the standard-dose protocol, our images reconstructed from 2D projected images and 3D ground-truth CT images showed good agreement in terms of structural similarity (SSIM, 0.7675 on average), peak signal-to-noise ratio (PSNR, 25.89 dB on average), mean absolute error (MAE, 0.02364 on average), and normalized root mean square error (NRMSE, 0.05731 on average). Moreover, with the ultra-low-dose protocol, our images reconstructed from 2D projected images and the 3D ground-truth CT images also showed good agreement in terms of SSIM (0.7008 on average), PSNR (23.58 dB on average), MAE (0.02991 on average), and NRMSE (0.07349 on average).

This study aimed to explore the relationship between thyroid-stimulating hormone (TSH) elevation and the baseline computed tomography (CT) density and volume of the thyroid. We examined 86 cases with new-onset hypothyroidism (TSH > 4.5 IU/mL) and 1071 controls from a medical check-up database over 5 years. A deep learning-based thyroid segmentation method was used to assess CT density and volume. Statistical tests and logistic regression were employed to determine differences and odds ratios. Initially, the case group showed a higher CT density (89.8 vs. 81.7 Hounsfield units (HUs)) and smaller volume (13.0 vs. 15.3 mL) than those in the control group. For every +10 HU in CT density and −3 mL in volume, the odds of developing hypothyroidism increased by 1.40 and 1.35, respectively. Over the course of the study, the case group showed a notable CT density reduction (median: −8.9 HU), whereas the control group had a minor decrease (−2.9 HU). Thyroid volume remained relatively stable for both groups. Higher CT density and smaller thyroid volume at baseline are correlated with future TSH elevation. Over time, there was a substantial and minor decrease in CT density in the case and control groups, respectively. Thyroid volumes remained consistent in both cohorts.

Abstract Meteorological-tsunami-like (or meteotsunami-like) periodic oscillation was muographically detected with the Tokyo-Bay Seafloor Hyper-Kilometric Submarine Deep Detector (TS-HKMSDD) deployed in the underwater highway called the Trans-Tokyo Bay Expressway or Tokyo Bay Aqua-Line (TBAL). It was detected right after the arrival of the 2021 Typhoon-16 that passed through the region 400 km south of the bay. The measured oscillation period and decay time were respectively 3 h and 10 h. These measurements were found to be consistent with previous tide gauge measurements. Meteotsunamis are known to take place in bays and lakes, and the temporal and spatial characteristics of meteotsunamis are similar to seismic tsunamis. However, their generation and propagation mechanisms are not well understood. The current result indicates that a combination of muography and trans-bay or trans-lake underwater tunnels will offer an additional tool to measure meteotsunamis at locations where tide gauges are unavailable.

Diagnostic radiologists need artificial intelligence (AI) for medical imaging, but access to medical images required for training in AI has become increasingly restrictive. To release and use medical images, we need an algorithm that can simultaneously protect privacy and preserve pathologies in medical images. To address this, we introduce DP-GLOW, a hybrid that combines the local differential privacy (LDP) algorithm with GLOW, one of the flow-based deep generative models. By applying a GLOW model, we disentangle the pixelwise correlation of images, which makes it difficult to protect privacy with straightforward LDP algorithms for images. Specifically, we map images to the latent vector of the GLOW model, where each element follows an independent normal distribution. We then apply the Laplace mechanism to this latent vector to achieve ϵ-LDP, which is one of the LDP algorithms. Moreover, we applied DP-GLOW to chest X-ray images to generate LDP images while preserving pathologies. The ϵ-LDP-processed chest X-ray images obtained with DP-GLOW indicate that we have obtained a powerful tool for releasing and using medical images for training AI.

IntroductionNon-technical skills are essential for surgical patient safety and are implemented in clinical practice. However, training for non-technical skills has not been thoroughly investigated. This study aimed to evaluate the learning curve for non-technical skill-based education in herniorrhaphy.MethodsQuality improvement initiatives, including non-technical skill-based intervention, were performed in the department of surgery. The intervention included declaring the patient safety policy, briefing and debriefing, and criterion for the switching of places of the trainee and instructor as defined by the department. Patients who underwent herniorrhaphy from April 2014 to September 2017 were included.ResultsA total of 14 trainees and nine instructors in the pre-intervention period and 14 trainees and seven instructors in the intervention period were included in this study. The median experience of each trainee was 28 and 15 cases in the pre-intervention and intervention groups, respectively. A total of 749 patients were included: 473 in the pre-intervention period and 328 in the intervention period. Demographics and hernia types were mostly similar between groups, and morbidity was not statistically different between the two groups (3.4 vs. 1.2%, p = 0.054). The nonlinear regression model showed an early decline and deep plateau phase of the learning curve in the intervention group. A significant difference was observed in the plateau operation time (61 min in the pre-intervention group and 52 min in the intervention group).ConclusionThis study demonstrated the effectiveness of non-technical skill-based intervention for surgical training. An early decline and deep plateau of the learning curve can be achieved with well-implemented quality improvement initiatives. Nonetheless, further studies are needed to establish a training program for non-technical skill-based learning.

Abstract Background It is essential for radiologists to communicate actionable findings to the referring clinicians reliably. Natural language processing (NLP) has been shown to help identify free-text radiology reports including actionable findings. However, the application of recent deep learning techniques to radiology reports, which can improve the detection performance, has not been thoroughly examined. Moreover, free-text that clinicians input in the ordering form (order information) has seldom been used to identify actionable reports. This study aims to evaluate the benefits of two new approaches: (1) bidirectional encoder representations from transformers (BERT), a recent deep learning architecture in NLP, and (2) using order information in addition to radiology reports. Methods We performed a binary classification to distinguish actionable reports (i.e., radiology reports tagged as actionable in actual radiological practice) from non-actionable ones (those without an actionable tag). 90,923 Japanese radiology reports in our hospital were used, of which 788 (0.87%) were actionable. We evaluated four methods, statistical machine learning with logistic regression (LR) and with gradient boosting decision tree (GBDT), and deep learning with a bidirectional long short-term memory (LSTM) model and a publicly available Japanese BERT model. Each method was used with two different inputs, radiology reports alone and pairs of order information and radiology reports. Thus, eight experiments were conducted to examine the performance. Results Without order information, BERT achieved the highest area under the precision-recall curve (AUPRC) of 0.5138, which showed a statistically significant improvement over LR, GBDT, and LSTM, and the highest area under the receiver operating characteristic curve (AUROC) of 0.9516. Simply coupling the order information with the radiology reports slightly increased the AUPRC of BERT but did not lead to a statistically significant improvement. This may be due to the complexity of clinical decisions made by radiologists. Conclusions BERT was assumed to be useful to detect actionable reports. More sophisticated methods are required to use order information effectively.

Abstract Tidal measurements are of great significance since they may provide us with essential data to apply towards protection of coastal communities and sea traffic. Currently, tide gauge stations and laser altimetry are commonly used for these measurements. On the other hand, muography sensors can be located underneath the seafloor inside an undersea tunnel where electric and telecommunication infrastructures are more readily available. In this work, the world’s first under-seafloor particle detector array called the Tokyo-bay Seafloor Hyper-Kilometric Submarine Deep Detector (TS-HKMSDD) was deployed underneath the Tokyo-Bay seafloor for conducting submarine muography. The resultant 80-day consecutive time-sequential muographic data were converted to the tidal levels based on the parameters determined from the first-day astronomical tide height (ATH) data. The standard deviation between ATH and muographic results for the rest of a 79-day measurement period was 12.85 cm. We anticipate that if the length of the TS-HKMSDD is extended from 100 m to a full-scale as large as 9.6 km to provide continuous tidal information along the tunnel, this muography application will become an established standard, demonstrating its effectiveness as practical tide monitor for this heavy traffic waterway in Tokyo and in other important sea traffic areas worldwide.

Lung cancer is the second most common cancer in the world, with an average five-year survival rate of 15 percent. Approximately 238,340 people were diagnosed in the US in 2023 based on the estimation of the American Cancer Society, and 127,070 people died from it. Cancer has always been a big problem for scientists. There has never been a good solution. So, the early detection of cancer is particularly important. In recent years, endobronchial ultrasonography (EBUS) images have been used more and more in the diagnosis of lung cancer because of their advantages of good real-time performance, no radiation, and superior performance. This research aims to develop a computer-aided diagnosis (CAD) system to differentiate benign and malignant peripheral pulmonary lesions (PPLs). The efficacy of this framework was evaluated on a dataset comprising 69 cases of lung carcinoma, encompassing 59 malignant instances and 10 benign cases. The final experimental results of accuracy, F1-Score, AUC, PPV, NPV, sensitivity, and specificity were 0.7, 0.63, 0.75, 0.84, 0.68, 0.56, and 0.85, respectively. From the experiment results, the developed CAD system has the potential ability to diagnose PPLs by using the EBUS images based on Deep Learning.

Precisely detecting puncture times has long posed a challenge in medical education. This challenge is attributable not only to the subjective nature of human evaluation but also to the insufficiency of effective detection techniques, resulting in many medical students lacking full proficiency in injection skills upon entering clinical practice. To address this issue, we propose a novel detection method that enables automatic detection of puncture times during injection without needing wearable devices. In this study, we utilized a hardware system and the YOLOv7 algorithm to detect critical features of injection motion, including puncture time and injection depth parameters. We constructed a sample of 126 medical injection training videos of medical students, and skilled observers were employed to determine accurate puncture times. Our experimental results demonstrated that the mean puncture time of medical students was 2.264 s and the mean identification error was 0.330 s. Moreover, we confirmed that there was no significant difference (p = 0.25 with a significance level of α = 0.05) between the predicted value of the system and the ground truth, which provides a basis for the validity and reliability of the system. These results show our system’s ability to automatically detect puncture times and provide a novel approach for training healthcare professionals. At the same time, it provides a key technology for the future development of injection skill assessment systems.

This study examines 4β-Hydroxycholesterol (4β-HC), which is considered to be a potential marker for the CYP3A4 induction of new chemical entities (NCEs) in drug development. To ensure the use of 4β-HC as a practical biomarker, it is necessary to accurately measure 4β-HC and demonstrate that CYP3A4 induction can be appropriately assessed, even for weak inducers. In clinical trials of NCEs, plasma is often collected with various anticoagulants, in some cases, the plasma is acidified, then stored for an extended period. In this study, we examined the effects of these manipulations on the measurement of 4β-HC, and based on the results, we optimized the plasma collection and storage protocols. We also found that a cholesterol oxidation product is formed when plasma is stored, and by monitoring the compound, we were able to identify when plasma was stored inappropriately. After evaluating the above, clinical drug–drug interaction (DDI) studies were conducted using two NCEs (novel retinoid-related orphan receptor γ antagonists). The weak CYP3A4 induction by the NCEs (which were determined based on a slight decline in the systemic exposure of a probe substrate (midazolam)), was detected by the significant increase in 4β-HC levels (more specifically, 4β-HC/total cholesterol ratios). Our new approach, based on monitoring a cholesterol oxidation product to identify plasma that is stored inappropriately, allowed for the accurate measurement of 4β-HC, and thus, it enabled the evaluation of weak CYP3A4 inducers in clinical studies without using a probe substrate.

Abstract Background It has been reported that median arcuate ligament syndrome is closely associated with gastric or pancreaticoduodenal artery aneurysms. Hemodynamic state plays an important role in the formation of the aneurysms. These aneurysms are treated with open resection or endovascular exclusion. However, whether revascularization of the celiac artery can prevent the aneurysm formation is unknown. This report indicated a possibility that prophylactic revascularization for celiac artery stenosis resulted in decreased shear stress on the collaterals, which may otherwise be susceptible to new aneurysms. Case presentation This report describes a 51-year-old man who presented with epigastric pain at our hospital. According to contrast enhanced computed tomography (CT), he was diagnosed with a ruptured right gastric artery aneurysm and celiac artery stenosis caused by the median arcuate ligament (MAL). He had a vascular anomaly of the common hepatic artery arising from the superior mesenteric artery (SMA). His vital signs were stable. We informed him of the situation and he chose open surgery rather than endovascular treatment. Following, we resected the aneurysm and transected the MAL. Intraoperative angiography after transection of the MAL showed the antegrade blood flow to the splenic artery instead of the retrograde flow via the prominent collaterals. Follow-up CT confirmed narrowed collateral vessels between the SMA and the celiac artery without de-novo aneurysms. Conclusion While the necessity of celiac artery release could be questioned, the present case supports the hemodynamic benefits of MAL transection in terms of de-novo aneurysm prevention.

A 76-year-old man was diagnosed with abdominal aortic aneurysm and a left-sided inferior vena cava. He underwent open surgery, and we employed the Cattell-Braasch maneuver to approach the abdominal aortic aneurysm from the right side. This enabled securing of the abdominal aortic aneurysm neck without mobilizing or dissecting the inferior vena cava. His postoperative course was uneventful. Although abdominal aortic aneurysm is typically approached from the left side in open surgery, approaching from the right side is beneficial in patients with abdominal aortic aneurysm and a left-sided inferior vena cava.

Iliopsoas muscle hematoma in a patient with alcoholic liver cirrhosis is rarely seen, however it has a high mortality. Thus we should cautiously make a diagnosis and treatment. This is the case of a 60-year-old male. He had a 15-year history of alcoholic liver disease and emphysema. He presented with low back pain after a fall that had happened 2 months before. Due to persistent back pain, he went to see a local physician who, after detailed examination, suspected rupture of bilateral common iliac artery aneurysms and transferred the patient to our hospital. The same presumptive diagnosis was made, and on this basis, an aortic bifemoral Y-graft was implanted. He developed aspiration pneumonia and hepatic and renal dysfunction postoperatively, which led to multiple organ failure and subsequent in-hospital death on postoperative day 62. This was believed to be a case of iliopsoas muscle hematoma developed in a patient with liver cirrhosis, and considering it was a case with poor surgical risk, a conservative treatment option such as transcatheter arterial embolization should also have been considered. Although iliopsoas muscle hematoma with alcoholic liver cirrhosis is rare, an appropriate treatment plan should be determined on a case-by-case basis despite its poor prognosis.

Colonic angiodysplasia (AD) with bleeding as a comorbidity in the aging population is being increasingly reported. However, to our knowledge, there is no report on colonic AD accompanied by a huge hematoma. Herein, we report a case of colonic AD with a huge submucosal hematoma. A 75-year-old man with sudden melena was referred to our hospital. Helical computed tomographic angiography (CTA) revealed bleeding from the sigmoid colon. Additionally, colonoscopy showed a huge submucosal hematoma with bleeding in the sigmoid colon. As endoscopic hemostasis was difficult, sigmoidectomy was performed. The pathological diagnosis was colonic AD. The present case indicates that colonic AD should be considered in the differential diagnosis for melena. In addition, the case shows that helical CTA, which is a noninvasive imaging modality, is useful for the diagnosis of colonic AD and is as effective as colonoscopy and angiography for diagnosis.

This report describes the results of meteorological observations at Syowa Station from February 1st, 2007 to January 31st, 2008, carried out by the Meteorological Observation Team of the 48th Japanese Antarctic Research Expedition(JARE-48). The observation methods, instruments and statistical methods used by JARE-48 were almost the same as those used by the JARE-47 observation team. Remarkable weather phenomena observed during the period of JARE-48 are as follows.1) In October 2007, there were 6 blizzards and 15 blizzard days, and the monthly mean temperature was -10.8℃, which was the highest record for Syowa Station.2) In November 2007, the monthly sunshine duration at Syowa Station was 474.8 hours, which was the longest record, and minimum humidity 21% updated minimum.3) The monthly mean amount of total ozone over Syowa Station in July was 233 m atm-cm, which was the lowest record. The amount of total ozone became lower than or equal to 220 m atm-cm from mid-August to end of October. On October 5th, the amount of total ozone was 138 m atm-cm, which was the lowest value in 2007.