Journal of the Meteorological Society of Japan最新文献

Background: The clinical and serologic heterogeneity of systemic lupus erythematosus (SLE) presents challenges for diagnosis, particularly in the earliest stages of the disease when there are insufficient signs to make a reliable diagnosis.

Aim: To make a comparative assessment of sensitivity and specificity of various classification criteria of SLE on a cohort of patients of Nasonova Research Institute of Rheumatology.

Materials and methods: A total of 252 patients were included in the study; 152 (60%) of 252 patients had reliable SLE (mean age 36 [29.5-46] years, duration of disease 9 [3.4-19] years). Of 252 patients, 26 (11%) had PAPS (mean age 36.5 [31-42] years, duration of disease 4.6 [1-10.4] years). Systemic sclerosis was diagnosed in 74/252 (29%) patients, (mean age 51.5 [42-59] years, duration of disease 9 [5-16] years). The quality of the classification function of the criteria was assessed by ROC analysis.

Results: SLE was diagnosed in 131 (86%) of 152 patients using the American College of Rheumatology - ACR)-1997 criteria, in 145 (95%) using the The Systemic Lupus International Collaborating Clinics (SLICC) 2012 criteria, and in 144 (94.7%) using the European League Against Rheumatism (EULAR)/ACR 2019 criteria. ANF positivity was the least statistically significant of all signs in relation to the diagnosis of SLE. The area under the curve (AUC) for ANF≥1/160 titers was AUC 0.654 for the ACR-97 criteria, AUC 0.616 for the SLICC-12 SLE criteria, and AUC 0.609 for the 2019 EULAR/ACR criteria. ROC analysis of the relationship between the number of criteria/points and a reliable diagnosis of SLE revealed a high diagnostic accuracy - the AUC for all SLE criteria was greater than 0.940. In the ROC analysis of patients with SLE and PAFS, indicating the number of diagnostic criteria, sensitivity was 86% for ACR-1997, 95% for SLICC-2012, 95% for EULAR/ACR 2019, and specificity was 100, 62 and 62%, respectively.

Conclusion: The classification criteria SLICC-2012 and EULAR/ACR 2019 are more sensitive for the diagnosis of SLE in the Russian population, and the criteria ACR-1997 are more specific. All three variants of the SLE classification criteria have sufficient sensitivity and specificity for their use in real clinical practice.

Dairy cows are usually culled and transported from the farm when they no longer meet the farm's standards for production or are not needed for milk production. Some cows are transported while in poor condition and may deteriorate further during transport. In February 2020, Canadian federal animal transport regulations were revised with the aim to minimise risks to livestock during transport; changes that may impact cull dairy cows included defining compromised cattle and limiting their maximum transport time. This study conducted semi-structured interviews with dairy farmers (n = 6) and cattle haulers (n = 4) in British Columbia, Canada, to gain an in-depth understanding of the effect of the regulations on their practices when shipping and transporting dairy cows to slaughter. Interviews were transcribed in Otter.ai and thematically coded in NVivo 12. While farmer and hauler participants recognised the importance of animal welfare during transport and described practices such as shipping mobile animals to reduce the risk that cows would become non-ambulatory during transport, they also described little change in shipping and transport practices due to the new regulations. Among interviewed participants, barriers to compliance with the regulations appear to be low knowledge of, and mixed or negative attitudes towards the regulations. Participants also described how they felt a lack of communication along the transport chain and limited transport and slaughter infrastructure made compliance difficult. Possible suggestions to remedy these barriers include providing educational resources about the regulations and encouraging communication about cow fitness for transport between responsible parties in the transport chain.

Vaginitis is a prevalent gynecologic disease that threatens millions of women’s health. Although microscopic examination of vaginal discharge is an effective method to identify vaginal infections, manual analysis of microscopic leucorrhea images is extremely time-consuming and labor-intensive. To automate the detection and identification of visible components in microscopic leucorrhea images for early-stage diagnosis of vaginitis, we propose a novel end-to-end deep learning-based cells detection framework using attention-based detection with transformers (DETR) architecture. The transfer learning was applied to speed up the network convergence while maintaining the lowest annotation cost. To address the issue of detection performance degradation caused by class imbalance, the weighted sampler with on-the-fly data augmentation module was integrated into the detection pipeline. Additionally, the multi-head attention mechanism and the bipartite matching loss system of the DETR model perform well in identifying partially overlapping cells in real-time. With our proposed method, the pipeline achieved a mean average precision (mAP) of 86.00% and the average precision (AP) of epithelium, leukocyte, pyocyte, mildew, and erythrocyte was 96.76, 83.50, 74.20, 89.66, and 88.80%, respectively. The average test time for a microscopic leucorrhea image is approximately 72.3 ms. Currently, this cell detection method represents state-of-the-art performance.