Initiatives for immune-related adverse events by the outpatient pharmacist clinic

Early detection is the key in managing side effects because immune-related adverse events (irAEs) are becoming more serious, and their onset time differs. In our hospital, we conducted an outpatient pharmacist clinic for early detection of irAEs by self-care practice for the cases of immune checkpoint inhibitor administration. As a result of a retrospective survey of 207 cases, the percentage of irAEs found by pharmacist’s suggestion of the outpatient pharmacist clinic increased over time, and a high detection ratio was obtained even for irAEs with a late onset time. The incidence of serious irAEs was higher than that in the immediate post-marketing surveillance, and different factors were considered. Although there were some problems, the outpatient pharmacist clinic had a certain effect.

Security Testing Framework for Web Applications

The growth of web-based applications has increased tremendously from last two decades. While these applications bring huge benefits to society, yet they suffer from various security threats. Although there exist various techniques to ensure the security of web applications, still a large number of applications suffer from a wide variety of attacks and result in financial loses. In this article, a security-testing framework for web applications is proposed with an argument that security of an application should be tested at every stage of software development life cycle (SDLC). Security testing is initiated from the requirement engineering phase using a keyword-analysis phase. The output of the first phase serves as input to the next phase. Different case study applications indicate that the framework assists in early detection of security threats and applying appropriate security measures. The results obtained from the implementation of the proposed framework demonstrated a high detection ratio with a less false-positive rate.

PLRSNet: a semantic segmentation network for segmenting plant leaf region under complex background

PurposeExtraction of leaf region from the plant leaf images is a prerequisite process for species recognition, disease detection and classification and so on, which are required for crop management. Several approaches were developed to implement the process of leaf region segmentation from the background. However, most of the methods were applied to the images taken under laboratory setups or plain background, but the application of leaf segmentation methods is vital to be used on real-time cultivation field images that contain complex backgrounds. So far, the efficient method that automatically segments leaf region from the complex background exclusively for black gram plant leaf images has not been developed.Design/methodology/approachExtracting leaf regions from the complex background is cumbersome, and the proposed PLRSNet (Plant Leaf Region Segmentation Net) is one of the solutions to this problem. In this paper, a customized deep network is designed and applied to extract leaf regions from the images taken from cultivation fields.FindingsThe proposed PLRSNet compared with the state-of-the-art methods and the experimental results evident that proposed PLRSNet yields 96.9% of Similarity Index/Dice, 94.2% of Jaccard/IoU, 98.55% of Correct Detection Ratio, Total Segmentation Error of 0.059 and Average Surface Distance of 3.037, representing a significant improvement over existing methods particularly taking into account of cultivation field images.Originality/valueIn this work, a customized deep learning network is designed for segmenting plant leaf region under complex background and named it as a PLRSNet.

A Deep Learning-Based Embedding Framework for Object Detection and Recognition in Underwater Marine Organisms

The detection of marine organisms is an important part of the intelligent strategy in marine ranch, which requires an underwater robot to detect the marine organism quickly and accurately in the complex ocean environment. Based on the latest deep learning arithmetic, this paper put forward to find the marine organism in a picture or video to construct a real-time objective invention system for marine organisms. The neural network arithmetic： YOLOv4 was employed to extract the deep features of marine organisms, implementing the accurate detection and size detection of different fish can use arithmetic for evaluation in fisheries. Furthermore, improving the architecture of the backbone and the neck connection is called YOLOv4-embedding. As a result, compared with other object detection arithmetic, YOLOv4-embedding object detection arithmetic was better at detection accuracy--higher detection confidence and higher detection ratio than other one-stage object detection arithmetic, EfficientDet-D3 example. The consequence demonstrates that the suggested instrument could implement the rapid invention of different varieties in marine organisms. Compared to the YOLOv4, the mAP 75 of the YOLOv4-embedding achieves an improvement of 2.92% for the marine organism dataset at a rapid rate of ~51 FPS on RTX 3090, 60.8% AP 50 for the MS COCO dataset.

Assessment of the fatality rate and transmissibility taking account of undetected cases during an unprecedented COVID-19 surge in Taiwan

Background During the COVID-19 outbreak in Taiwan between May 11 and June 20, 2021, the observed fatality rate (FR) was 5.3%, higher than the global average at 2.1%. The high number of reported deaths suggests that hospital capacity was insufficient. However, many unexplained deaths were subsequently identified as cases, indicating that there were a few undetected cases, hence resulting in a higher estimate of FR. Knowing the number of total infected cases can allow an accurate estimation of the fatality rate (FR) and effective reproduction number (Rt). Methods After adjusting for reporting delays, we estimated the number of undetected cases using reported deaths that were and were not previously detected. The daily FR and Rt were calculated using the number of total cases (i.e. including undetected cases). A logistic regression model was developed to predict the detection ratio among deaths using selected predictors from daily testing and tracing data. Results The estimated true daily case number at the peak of the outbreak on May 22 was 897, which was 24.3% higher than the reported number, but the difference became less than 4% on June 9 and afterward. After taking account of undetected cases, our estimated mean FR (4.7%) was still high but the daily rate showed a large decrease from 6.5% on May 19 to 2.8% on June 6. Rt reached a maximum value of 6.4 on May 11, compared to 6.0 estimated using the reported case number. The decreasing proportion of undetected cases was associated with the increases in the ratio of the number of tests conducted to reported cases, and the proportion of cases that are contact-traced before symptom onset. Conclusions Increasing testing capacity and tracing efficiency can lead to a reduction of hidden cases and hence improvement in epidemiological parameter estimation.

Does Being a Refugee Increase the Possibility of Complicated Appendicitis?

Background; Refugees may have problems in recognizing their illnesses and accessing treatment due to communication and sociocultural factors. Objectives; In this study, we aimed to present whether there is a difference in complicated appendicitis rates between Turkish and refugee patients. Methods; A total of 563 patients who underwent appendectomy surgery in our hospital between September 2018 and June 2020 and met the study criteria were examined. The patients were divided into two groups. Group-1 constituted of the Turkish patients, and Group-2 constituted of the refugee patients. The demographic, clinical, and histopathological characteristics of the patients were compared. Results; Group-1 had 489 (86.9%) patients, while Group-2 constituted of 74 (13.1%) patients. There were 278 (56.9%) male patients in Group-1 and 36 (48.6%) male patients in Group-2. Turkish patients' median age was 28 (18-81), while the median age of refugee patients was 27 (18-75). Intraoperative perforation detection ratio, open appendectomy ratio, preoperative C-reactive protein level, histopathological gangrenous or perforated appendicitis ratio and postoperative hospital stay length were found higher in the refugee patient group (p<0.05). Conclusion; Refugee patients are intense in countries such as Turkey; We believe that general surgery specialists should consider the possibility of complicated appendicitis in refugee patients scheduled for surgery for acute appendicitis.

Adjusting Reported COVID-19 Deaths for the Prevailing Routine Death Surveillance in India

In India, the “low mortality” narrative based on the reported COVID-19 deaths may be causing more harm than benefit. The extent to which COVID-19 deaths get reported depends on the coverage of routine death surveillance [death registration along with medical certification of cause of death (MCCD)] and the errors in MCCD. In India, the coverage of routine death surveillance is 18.1%. This is compounded by the fact that COVID-19 death reporting is focused among reported cases and the case detection ratio is low. To adjust for the coverage of routine death surveillance and errors in MCCD, we calculated a correction (multiplication) factor at national and state level to produce an estimated number of COVID-19 deaths. As on July 31, 2020, we calculated the infection fatality ratio (IFR) for India (0.58:100–1.16:100) using these estimated COVID-19 deaths; this is comparable with the IFR range in countries with near perfect routine death surveillance. We recommend the release of excess deaths data during COVID-19 (at least in states with high death registration) and post-mortem COVID-19 testing as a surveillance activity for a better understanding of under-reporting. In its absence, we should adjust reported COVID-19 deaths for the coverage of routine death surveillance and errors in MCCD. This way we will have a clear idea of the true burden of deaths and our public health response will never be inadequate. We recommend that “reported” or “estimated” is added before the COVID-19 death data and related indicators for better clarity and interpretation.

Characterization Of A Single Pixel Beta Detector For Guidance In Breast-Conserving Surgery

Breast-conserving surgery (BCS) is a challenging surgical procedure due to the lack of intraoperative image guidance available to surgeons. One potential method of intra-operative guidance would be radio-guided surgery with adiopharmaceutical emitting beta particles. In this thesis, a single pixel beta sensitive detector was constructed and characterized for intra-operative guidance during BCS. The thickness of the scintillation element of the detector was optimized to obtain a superior beta to gamma detection ratio. A computer model of the detector response was derived from an empirically measured, two-dimensional (2D) detector response. An in silico study evaluated whether the novel single pixel beta detector could detect less than 1 mm² deposits of cancer at the cut edge of the surgically excised cancerous tissue, with a sensitivity and specificity of 95%. A thickness of 0.5 mm for a CaF₂(Eu) scintillator was found to be optimal for a beta to gamma detection ratio. Additionally, according to an in silico study it is expected that with an acquisition time of 30 seconds, a tumour-to-background ratio of 5 or higher, and a normal breast tissue activity of 1.69 kBq/ml, detection of cancerous deposits of less than 1 mm² is possible. The result of this thesis demonstrate that radio-guided BCS, with a CaF₂(Eu) scintillation beta particle detector, can intra-operatively assess the tumour margin involvement, which would help surgeons in determining resection margins.