Simulations of Future Trends in Welfare-Centric Egg Production Systems: Impacts on Productivity and Prices.

The implementation of more animal welfare-centric livestock production systems holds considerable potential for achieving sustainable production in Japan. However, such systems generally reduce productivity per unit area, impose financial burdens on farmers that adversely affect their physical and mental health, and require increased consumer awareness. In particular, declines in productivity per unit area substantially affect the farming economy, giving rise to concerns about the sufficient egg production for the population, which consequently exacerbates Japan's food security concerns. In this study, our goal was to predict temporal trends in the Japanese transition to welfare-centric facilities by analyzing desired egg consumption. We formulated five scenarios, ending in 2050, to assess the future impact of changing the proportions of types of egg production on total production, numbers of hens, total farm area, production costs, and retail prices. Multiple issues were identified in our analysis. First, Japanese production uses conventional caging, which provides high efficiency, maximum output, and decreased prices. Our simulations suggest that ensuring optimal production is likely to present challenges as animal welfare is addressed. They also suggest that the poultry industry or the Japanese government will likely need to plan changes in production methods will likely be necessary to prevent egg surpluses and/or shortages. Additional issues were identified, including price increases, numbers of hens, total farm area, and production costs. This study offers valuable insights for policymakers in their decision-making processes to help optimize production methods while considering food security, animal welfare, and farmers' welfare.

Pediatric intraparenchymal meningioma in the basal ganglia treated with gross total resection: a case report and review of the literature.

INTRODUCTION: Intraparenchymal meningiomas in the basal ganglia are extremely rare, and to the best of our knowledge, only three case reports have been published to date. Owing to concerns regarding major vessels, gross total resection (GTR) is difficult to achieve; therefore, subtotal resection and radiation therapy are often chosen as treatment options. We present a pediatric case with an intraparenchymal meningioma in the left basal ganglia that was successfully treated with GTR. We also reviewed the relevant literature to discuss the pathogenesis, radiological findings, and treatment methods of this rare disease. CASE REPORT: A 4-year-old girl presented with progressive right facial paralysis, aphasia, and right incomplete hemiplegia. Imaging revealed a mass lesion in the left basal ganglia and unilateral obstructive hydrocephalus. Neuroendoscopic septostomy, tumor biopsy, and cerebrospinal fluid reservoir placement were performed, and the initial pathological diagnosis was suspected glioma. Thus, craniotomy was performed to remove the tumor, which was white, elastic, and well-defined. Intraoperative rapid pathology revealed a meningioma. Postoperatively, the patient experienced transient worsening of the right incomplete hemiplegia, which subsequently improved. The final pathological diagnosis was a fibrous meningioma. CONCLUSION: Surgery for intraparenchymal meningiomas in the basal ganglia is challenging owing to the proximity of major blood vessels; however, GTR may be preferable to subtotal resection, considering the possibility of recurrence. Even in cases of intraparenchymal tumors, it is important to consider meningioma as a differential diagnosis and to carefully plan the appropriate treatment.

Ruptured traumatic posterior inferior cerebellar artery pseudoaneurysm: A case report and literature review.

Background: Traumatic intracranial aneurysm (TICA) accounts for approximately 1% of cerebral aneurysms. There are few reports of TICA limited to the posterior inferior cerebellar artery (PICA-TICA). Case Description: A 69-year-old woman fell into a shallow river, bruising her head and chest, and was admitted to our emergency department with disorientation. Computed tomography (CT) showed subarachnoid hemorrhage (SAH), intraventricular hemorrhage (IVH), left temporal lobe contusion, and fractures of the right temporal bone. A cerebral CT angiogram revealed no vascular abnormalities or aneurysms. The patient was in a semi-comatose state 2 h later, and CT showed worsening SAH. A cerebral angiogram revealed an 11 mm aneurysm of the anterior medullary segment of the right PICA. We attempted intra-aneurysmal embolization intending to preserve the PICA, but the aneurysmal neck was thin, and the microcatheter could not be placed in a stable position. Therefore, n-butyl-2-cyanoacrylate (NBCA) was injected to embolize the aneurysm. When the microcatheter was removed, NBCA was scattered distally in the PICA, and the distal PICA was occluded. The aneurysm could be embolized, but there was an increase in hemorrhagic contusion in the left temporal lobe. Decompression craniectomy was performed, but she died due to hemorrhagic contusion and uncal herniation 6 days after surgery. Conclusion: PICA-TICA is often accompanied by IVH and SAH, and there are some reports of cases with a vascular anomaly of the posterior circulation. Since TICA is at risk of rapid growth and rupture, an early and appropriate diagnosis is important.

Loop-mediated isothermal amplification (LAMP) and machine learning application for early pregnancy detection using bovine vaginal mucosal membrane.

An early and accurate pregnancy diagnosis method is required to improve the reproductive performance of cows. Here we developed an easy pregnancy detection method using vaginal mucosal membrane (VMM) with application of Reverse Transcription-Loop-mediated Isothermal Amplification (RT-LAMP) and machine learning. Cows underwent artificial insemination (AI) on day 0, followed by VMM-collection on day 17-18, and pregnancy diagnosis by ultrasonography on day 30. By RNA sequencing of VMM samples, three candidate genes for pregnancy markers (ISG15 and IFIT1: up-regulated, MUC16: down-regulated) were selected. Using these genes, we performed RT-LAMP and calculated the rise-up time (RUT), the first-time absorbance exceeded 0.05 in the reaction. We next determined the cutoff value and calculated accuracy, sensitivity, specificity, positive prediction value (PPV), and negative prediction value (NPV) for each marker evaluation. The IFIT1 scored the best performance at 92.5% sensitivity, but specificity was 77.5%, suggesting that it is difficult to eliminate false positives. We then developed a machine learning model trained with RUT of each marker combination to predict pregnancy. The model created with the RUT of IFIT1 and MUC16 combination showed high specificity (86.7%) and sensitivity (93.3%), which were higher compared to IFIT1 alone. In conclusion, using VMM with RT-LAMP and machine learning algorithm can be used for early pregnancy detection before the return of first estrus.