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On March 25th 2019, the Belle II detector recorded the first collisions delivered by the SuperKEKB accelerator. This marked the beginning of the physics run with vertex detector. The vertex detector was aligned initially with cosmic ray tracks without magnetic field simultaneously with the drift chamber. The alignment method is based on Millepede II and the General Broken Lines track model and includes also the muon system or primary vertex position alignment. To control weak modes, we employ sensitive validation tools and various track samples can be used as alignment input, from straight cosmic tracks to mass-constrained decays. With increasing luminosity and experience, the alignment is approaching the target performance, crucial for the first physics analyses in the era of Super-BFactories. We will present the software framework for the detector calibration and alignment, the results from the first physics run and the prospects in view of the experience with the first data.

The Belle II experiment features a substantial upgrade of the Belle detector and will operate at the SuperKEKB energy-asymmetric e+e− collider at KEK in Tsukuba, Japan. The accelerator completed its first phase of commissioning in 2016, and the Belle II detector saw its first electron-positron collisions in April 2018. Belle II features a newly designed silicon vertex detector based on double-sided strip layers and DEPFET pixel layers. A subset of the vertex detector was operated in 2018 to determine background conditions (Phase 2 operation). The collaboration completed full detector installation in January 2019, and the experiment started full data taking. This paper will report on the final arrangement of the silicon vertex detector part of Belle II with a focus on online monitoring of detector conditions and data quality, on the design and use of diagnostic and reference plots, and on integration with the software framework of Belle II. Data quality monitoring plots will be discussed with a focus on simulation and acquired cosmic and collision data.

We demonstrate that Blautia coccoides JCM1395T has the potential to be used for tumor-targeted live bacterial therapeutics. Prior to studying its in vivo biodistribution, a sample preparation method for reliable quantitative analysis of bacteria in biological tissues was required. Gram-positive bacteria have a thick outer layer of peptidoglycans, which hindered the extraction of 16S rRNA genes for colony PCR. We developed the following method to solve the issue; the method we developed is as follows. The homogenates of the isolated tissue were seeded on agar medium, and bacteria were isolated as colonies. Each colony was heat-treated, crushed with glass beads, and further treated with restriction enzymes to cleave DNAs for colony PCR. With this method, Blautia coccoides JCM1395T and Bacteroides vulgatus JCM5826T were individually detected from tumors in mice intravenously receiving their mixture. Since this method is very simple and reproducible, and does not involve any genetic modification, it can be applied to exploring a wide range of bacterial species. We especially demonstrate that Blautia coccoides JCM1395T efficiently proliferate in tumors when intravenously injected into tumor-bearing mice. Furthermore, these bacteria showed minimal innate immunological responses, i.e., elevated serum tumor necrosis factor α and interleukin-6, similar to Bifidobacterium sp., which was previously studied as a therapeutic agent with a small immunostimulating effect. [ABSTRACT FROM AUTHOR]

Live bacterial therapeutics is gaining attention, especially for cancer therapy, because anaerobic bacteria selectively grow inside the solid tumours. However, the effect of tumour structure and bacterial characteristics on the pharmacokinetics of tumours is unclear; therefore, we aimed to elucidate the effects of tumour structure and types of bacteria on tumoral bacterial growth. Using six mouse xenograft models, including stroma-rich tumours similar to clinical tumours, and two models of live bacterial therapeutics, Salmonella typhimurium VNP20009 and Escherichia coli DH5α, we investigated bacterial growth and distribution in tumours after intravenous administration. Rapid growth of E. coli was observed in HCT116 and other tumours with few collagens, blood vessels not covered by mural cells, and a cancer cell area proliferated disorderly, whereas tumours with contrasting features, such as BxPC-3, showed lower bacterial growth and a limited intratumor distribution. Alternatively, Salmonella typhimurium VNP20009, when successfully proliferated (the probability was approximately 50%), grew to 108 colony forming units/g tissue even in BxPC-3 tumours, and its intratumor distribution was extensive. This study suggests that the development of new methods to modify tumour structure will be essential for the development of anti-tumour clinical therapies based on live bacterial therapeutics. [ABSTRACT FROM AUTHOR]

Although the categorization of ultrasound using the Breast Imaging Reporting and Data System (BI‐RADS) has become widespread worldwide, the problem of inter‐observer variability remains. To maintain uniformity in diagnostic accuracy, we have developed a system in which artificial intelligence (AI) can distinguish whether a static image obtained using a breast ultrasound represents BI‐RADS3 or lower or BI‐RADS4a or higher to determine the medical management that should be performed on a patient whose breast ultrasound shows abnormalities. To establish and validate the AI system, a training dataset consisting of 4028 images containing 5014 lesions and a test dataset consisting of 3166 images containing 3656 lesions were collected and annotated. We selected a setting that maximized the area under the curve (AUC) and minimized the difference in sensitivity and specificity by adjusting the internal parameters of the AI system, achieving an AUC, sensitivity, and specificity of 0.95, 91.2%, and 90.7%, respectively. Furthermore, based on 30 images extracted from the test data, the diagnostic accuracy of 20 clinicians and the AI system was compared, and the AI system was found to be significantly superior to the clinicians (McNemar test, p < 0.001). Although deep‐learning methods to categorize benign and malignant tumors using breast ultrasound have been extensively reported, our work represents the first attempt to establish an AI system to classify BI‐RADS3 or lower and BI‐RADS4a or higher successfully, providing important implications for clinical actions. These results suggest that the AI diagnostic system is sufficient to proceed to the next stage of clinical application. [ABSTRACT FROM AUTHOR]

Due to the increasing complexity of cancer treatment, ensuring safety and maintaining the quality of life during treatment are important issues. Patient‐reported outcomes (PROs) in oncology are essential for assessing patient symptoms. A feasibility study was undertaken on breast cancer patients by building a PRO data collection system based on LINE, one of the most popular social network service applications in Japan. In this study, one or more predefined PRO questions for each breast cancer patient's clinical situation were sent to the patient's LINE application daily. The patient selected a predefined answer by tapping the screen, but no free‐text answers were allowed. Seventy‐three patients were enrolled. The median observation period was 435 days (84‐656 days), and the total number of PROs collected was 16,417, with a mean of 224.9 reports per patient. Patients on adjuvant endocrine therapy were notified of 2.5 questions per week, and the median number of responses per week and response rate were 2.387 (1.687‐11.627) and 95.5%, respectively. Analyzing the results by age group, the number of responses from those aged 60 and above was equal to or higher than that of the younger age group. It was also possible to track each patient's PROs accurately. These results suggested that the design of the system, based on an application used daily, instead of using specifically prepared applications for collecting electronic PROs, was the reason for the favorable acceptance from patients and the satisfactory response rate from all age groups, including the elderly. [ABSTRACT FROM AUTHOR]

Background: Impulse control disorders are detrimental neuropsychiatric symptoms of Parkinson's disease. Increased impulsivity is a predisposing factor for impulse control disorders and should therefore be controlled. Recently, mindfulness meditation as a non-drug therapy has been reported to be useful in improving neuropsychiatric symptoms, such as impulsivity. Methods: We performed a prospective single-arm, open-label pilot trial to investigate the effectiveness of mindfulness meditation to control impulsivity in patients with Parkinson's disease (UMIN clinical trials registry: UMIN000037779). Results: Twenty patients with Parkinson's disease were enrolled in an 8-week mindfulness meditation program. As a primary outcome, we investigated whether the score of the Barratt Impulsiveness Scale (BIS-11) was significantly reduced after the intervention. As an exploratory examination, functional connectivity changes were also assessed by resting-state functional magnetic resonance imaging. After the intervention, the BIS-11 score was decreased from 59.5 [55.6, 63.3] (mean [95% confidence interval]) to 55.2 [50.3, 60.1] (ΔBIS-11: -4.2, [-7.5, -0.9]). Functional connectivity was increased in the default mode network (DMN) at a cluster including the precuneus, posterior cingulate gyrus, and left posterior lobe (false discovery rate-adjusted p [FDR-p] = 0.046) and in the right frontoparietal network (FPN) at the medial frontal lobe (FDR-p = 0.039). Conclusions: This open-label, single-arm pilot study provided preliminary data for mindfulness meditation to control the impulsivity of patients with PD. A brief mindfulness meditation program may be effective in controlling impulsivity in PD and may change the functional connectivity of the DMN and right FPN. [ABSTRACT FROM AUTHOR]

Introduction. The Questionnaire for Impulsive-Compulsive Disorders in Parkinson's Disease (PD)-Rating Scale (QUIP-RS) was developed to assess the severity of impulsive and compulsive behaviors (ICBs) in PD. We aimed to validate the Japanese version of QUIP-RS and determine the characteristics of ICBs in Japan. Methods. We translated the QUIP-RS into Japanese, back-translated it to English, and obtained confirmation from the original author that the questionnaire remained appropriate. The participants for the validation study were 161 PD patients, identified by continuous sampling at two institutions, who were diagnosed with ICBs through a semistructured interview and completed the QUIP-RS-J. Sensitivity, specificity, and cutoff values were calculated using receiver operating characteristic (ROC) curves. Interinstitutional reliability and test-retest reliability were also assessed for a subset of participants. Results. Twenty-six (16.1%) participants were diagnosed with ICB. The optimal cutoff value of the QUIP-RS-J total score was 6, with area under the curve (AUC) = 0.889 and sensitivity/specificity of 0.92/0.71. Each subscale also showed high AUC (0.89–1.00), sensitivity (0.92–1.00), and specificity (0.71–1.00). Compared with the English version, the optimal cutoff point for binge eating was higher and hypersexuality lower. The total score tended to be higher when described by an informant. Conclusion. The present study validated the Japanese version of QUIP-RS. Use of QUIP-RS-J enables standardized assessment of ICBs and can be used in clinical research, including international multicenter studies. [ABSTRACT FROM AUTHOR]

Invasive lobular carcinoma (ILC) has a different treatment response from invasive ductal carcinoma (IDC). We assessed whether perioperative chemotherapy was associated with improved prognosis in patients with ILC. Retrospective data of women who underwent surgery for ILC were extracted from the SEER database. Subjects were divided into non‐chemotherapy and chemotherapy groups. Overall, 10 537 patients were included, and 2107 patients were stratified into each group after propensity score matching. Perioperative chemotherapy significantly improved 10‐year survival rates for ILC, particularly in patients with large tumor size and lymph node metastases. Perioperative chemotherapy is effective for ILC patients with proper selection. [ABSTRACT FROM AUTHOR]

In several recent studies on metastatic breast cancer (MBC), ligand binding domain mutations of the estrogen receptor, which is coded by the ESR1 gene, were induced by long-term endocrine therapy and resulted in acquired endocrine therapy resistance and poor outcomes. Knowledge of the association between the development of ESR1 mutation and the clinicopathologic features may guide the decision-making process of metastatic breast cancer treatment, including endocrine therapy. The aim of the present study was to evaluate the association between the development of ESR1 mutation and the clinicopathologic characteristics of patients with MBC. To evaluate the association between the development of ESR1 mutation and clinicopathologic features, a cohort of 22 patients with MBC were retrospectively analyzed using next generation sequencing. In 14 of 22 patients, four mutations were detected on the metastatic site, including Tyr537Ser, Glu542Asp, Leu536Arg and Arg548Cys. Univariate analysis demonstrated that the duration of aromatase inhibitor and selective estrogen receptor modulator treatment, as well as the age of treatment initiation for early-stage breast cancer, were significantly associated with the development of ESR1 mutation. ESR1 mutation was identified in all five patients who received selective estrogen receptor modulators in the adjuvant setting followed by aromatase inhibitors in the metastatic setting, as well as in two of the three patients who received no selective estrogen receptor modulators in adjuvant setting followed by aromatase inhibitors in the metastatic setting. In conclusion, the results of the present study suggested that administrating adjuvant selective estrogen receptor modulator followed by aromatase inhibitor for metastasis may increase the frequency of ESR1 mutation. [ABSTRACT FROM AUTHOR]