False Positive Value Research Articles

Low initial cerebral regional cerebral saturation (rSO2) unresponsive to intervention in cardiac surgery: When can we consider a false positive low rSO2 value?

Low initial cerebral regional cerebral saturation (rSO2) unresponsive to intervention in cardiac surgery: When can we consider a false positive low rSO2 value?

Application value of indocyanine green fluorescence imaging in guiding sentinel lymph node biopsy diagnosis of gastric cancer: Meta-analysis.

Gastric cancer is a common malignant tumor of the digestive system worldwide, and its early diagnosis is crucial to improve the survival rate of patients. Indocyanine green fluorescence imaging (ICG-FI), as a new imaging technology, has shown potential application prospects in oncology surgery. The meta-analysis to study the application value of ICG-FI in the diagnosis of gastric cancer sentinel lymph node biopsy is helpful to comprehensively evaluate the clinical effect of this technology and provide more reliable guidance for clinical practice. To assess the diagnostic efficacy of optical imaging in conjunction with indocyanine green (ICG)-guided sentinel lymph node (SLN) biopsy for gastric cancer. Electronic databases such as PubMed, Embase, Medline, Web of Science, and the Cochrane Library were searched for prospective diagnostic tests of optical imaging combined with ICG-guided SLN biopsy. Stata 12.0 software was used for analysis by combining the "bivariable mixed effect model" with the "midas" command. The true positive value, false positive value, false negative value, true negative value, and other information from the included literature were extracted. A literature quality assessment map was drawn to describe the overall quality of the included literature. A forest plot was used for heterogeneity analysis, and P < 0.01 was considered to indicate statistical significance. A funnel plot was used to assess publication bias, and P < 0.1 was considered to indicate statistical significance. The summary receiver operating characteristic (SROC) curve was used to calculate the area under the curve (AUC) to determine the diagnostic accuracy. If there was interstudy heterogeneity (I 2 > 50%), meta-regression analysis and subgroup analysis were performed. Optical imaging involves two methods: Near-infrared (NIR) imaging and fluorescence imaging. A combination of optical imaging and ICG-guided SLN biopsy was useful for diagnosis. The positive likelihood ratio was 30.39 (95%CI: 0.92-1.00), the sensitivity was 0.95 (95%CI: 0.82-0.99), and the specificity was 1.00 (95%CI: 0.92-1.00). The negative likelihood ratio was 0.05 (95%CI: 0.01-0.20), the diagnostic odds ratio was 225.54 (95%CI: 88.81-572.77), and the SROC AUC was 1.00 (95%CI: The crucial values were sensitivity = 0.95 (95%CI: 0.82-0.99) and specificity = 1.00 (95%CI: 0.92-1.00). The Deeks method revealed that the "diagnostic odds ratio" funnel plot of SLN biopsy for gastric cancer was significantly asymmetrical (P = 0.01), suggesting significant publication bias. Further meta-subgroup analysis revealed that, compared with fluorescence imaging, NIR imaging had greater sensitivity (0.98 vs 0.73). Compared with optical imaging immediately after ICG injection, optical imaging after 20 minutes obtained greater sensitivity (0.98 vs 0.70). Compared with that of patients with an average SLN detection number < 4, the sensitivity of patients with a SLN detection number ≥ 4 was greater (0.96 vs 0.68). Compared with hematoxylin-eosin (HE) staining, immunohistochemical (+ HE) staining showed greater sensitivity (0.99 vs 0.84). Compared with subserous injection of ICG, submucosal injection achieved greater sensitivity (0.98 vs 0.40). Compared with 5 g/L ICG, 0.5 and 0.05 g/L ICG had greater sensitivity (0.98 vs 0.83), and cT1 stage had greater sensitivity (0.96 vs 0.72) than cT2 to cT3 clinical stage. Compared with that of patients ≤ 26, the sensitivity of patients > 26 was greater (0.96 vs 0.65). Compared with the literature published before 2010, the sensitivity of the literature published after 2010 was greater (0.97 vs 0.81), and the differences were statistically significant (all P < 0.05). For the diagnosis of stomach cancer, optical imaging in conjunction with ICG-guided SLN biopsy is a therapeutically viable approach, especially for early gastric cancer. The concentration of ICG used in the SLN biopsy of gastric cancer may be too high. Moreover, NIR imaging is better than fluorescence imaging and may obtain higher sensitivity.

Fracture risk prediction in postmenopausal women from GO Study: the comparison between FRAX, Garvan, and POL-RISK algorithms.

The aim of the longitudinal, retrospective study was to compare three tools designed to assess fracture risk: FRAX, Garvan, and POL-RISK in their prediction of fracture incidence. The study group consisted of 457 postmenopausal women with a mean age of 64.21 ± 5.94years from the Gliwice Osteoporosis (GO) Study. Comprehensive data on clinical factors related to fractures were collected for all participants. Bone densitometry was performed at the proximal femur using the Prodigy device (GE, USA). Fracture risk was established using the FRAX, Garvan, and POL-RISK algorithms. Data on the incidence of osteoporotic fractures were collected over the last 10years. During the period of observation 72, osteoporotic fractures occurred in 63 subjects. For a preliminary comparison of the predictive value of analyzed diagnostic tools, the fracture risk threshold of 10% was used. For FRAX, the fracture probability exceeding 10% was observed only in 11 subjects who experienced fractures; thus, the fracture was properly predicted only in 22.9% of women. For Garvan, the respective value was 90.5%, and for POL-RISK, it was 98.4%. That gave a very low true positive value for FRAX and a very high false positive value for Garvan and POL-RISK. Based on ROC curves, new thresholds for high risk of fractures were established for each calculator separately: 6.3% for FRAX major fracture, 20.0% for Garvan any fracture, and 18.0% for POL-RISK any fracture. Such thresholds improve the diagnostic accuracy of all compared fracture prediction tools. The current study showed that different fracture risk assessment tools, although having similar clinical purposes, require different cut-off thresholds for making therapeutic decisions. Better identification of patients requiring therapy based on such an approach may help reduce the number of new fractures.

False-negative factors of percutaneous transluminal clamp biopsy for suspected malignant biliary stricture: 194 cases analyzed from a single center

ObjectiveTo study the predictive factors of false negatives in the diagnosis of biliary stricture (BS) by percutaneous transluminal clamp biopsy (PTCB).MethodFrom January 2016 to January 2021, 194 patients with a high suspicion of malignant tumors due to BS underwent PTCB during biliary drainage at our department. The final diagnosis was confirmed by postoperative pathology, other tissue or cell evidence, or medical imaging follow-up. Univariate and multivariate regression analyses were performed on the pathological results, summarizing the independent risk factors for false-negative value (FNV) to help further clinical diagnosis and treatment.ResultsOf the 194 cases, 176 and 18 cases were finally diagnosed as malignant and benign BS, respectively, compared to 144 and 50 cases by PTCB, including 32 false-negative cases. The sensitivity, specificity, false-positive value, and FNV of PTCB were 81.8%, 100%, 0%, and 18.2%, respectively. Multivariate analysis showed that non-cholangiocarcinoma BS was an independent risk factor for FNV of PTCB (odds ratio 7.5 (95% CI 1.74–32.6), p < 0.01).ConclusionPTCB is an effective minimally invasive interventional technique for BS diagnosis. Non-cholangiocarcinoma BS is an independent risk factor for FNV.Critical relevance statementIdentifying factors that are predictive of false-negative results by percutaneous transluminal clamp biopsy in the setting of biliary stricture may have a guiding effect on clinical practice.Key points• Factors predictive of false negatives in the diagnosis of biliary stricture etiology by PTCB may aid in the interpretation of results.• Non-cholangiocarcinoma BS is an independent risk factor for FNV on PTCB.• PTCB is an effective minimally invasive interventional technique for BS diagnosis.Graphical

Abstract 971: Validation of a tumor-naïve circulating tumor DNA (ctDNA) response monitoring panel in advanced non-small cell lung cancer (aNSCLC)

Abstract Introduction: Monitoring of ctDNA can be a minimally invasive complement to tumor imaging for assessing treatment effect. Technical limitations require methods to distinguish tumor signal from clonal hematopoiesis (CH), often including non-tumor sequencing. We developed a tumor-naïve panel (FoundationOne® Monitor) to quantify ctDNA tumor fraction (TF). TF analytical validation used peripheral blood mononuclear cell (PBMC) sequencing. We then leveraged plasma collected serially from patients with aNSCLC in the real-world (rw) Prospective Clinico-Genomic (PCG; NCT04180176) study to investigate the utility of TF for monitoring therapy (tx) response. Methods: TF was quantified using a combination of aneuploidy and variant allele frequencies of genomic alterations (GAs), while excluding CH mutations and aneuploidy using fragmentomic signal from cfDNA. In the PCG study, data from consenting patients were collected from electronic health records from 23 participating Flatiron Health Research Network sites. We analyzed plasma collected 6-15 weeks after start of tx in exploratory and validation cohorts per prespecified criteria. We defined molecular response (MR) as undetectable TF on tx regardless of baseline TF. Hazard ratios (HR) and 95% confidence Interval (CI) were calculated with univariate Cox proportional hazard regression. Results: For TF validation, 1135 samples separate from the PCG study with paired PBMC results were included. Overall, 24/4274 (0.56%) CH derived non-aneuploidy GAs detected in 1134 samples were falsely classified as somatic. Of these, 4 were detected among 317 samples with no tumor signal, resulting in a false positive TF value (specificity = 98.7%). CH derived aneuploidy was observed in 27 PBMCs and was appropriately filtered during TF estimation in all cases. Assessing the impact of CH aneuploidy filtering on sensitivity, we only identified 1/320 (0.31%) samples where non-CH derived aneuploidy was omitted. To assess clinical validity, 222 patients were analyzed from the PCG study. MR was assessed after a median of 10.8 weeks of tx (IQR 8.9-12.0). In a subset of 152 patients treated with physicians’ choice, MR was associated with favorable rw progression free survival (rwPFS: 9.4 v 2.8 months [mo]; HR = 0.30; 95% CI: [0.21-0.44]) and rw overall survival (rwOS: 22.0 v 7.5 mo; HR = 0.33 [0.22-0.49]). We validated this finding on 70 patients receiving immunotherapy (50 with chemo). Again, MR was associated with favorable rwPFS (9.0 v 2.8 mo; HR = 0.28 [0.16-0.51]) and rwOS (20.2 v 9.4 mo; HR = 0.42 [0.23-0.78]). Conclusions: We describe a highly specific tumor naïve algorithmic filtration of non-tumor signal to enable high confidence ctDNA quantification and MR assessment. On tx MR is associated with favorable outcomes. These findings may enable personalized tx approaches tailored to a patient’s risk of progression and downstream cancer morbidity. Citation Format: Anne C. Chiang, Russell W. Madison, Yanmei Huang, Alexander Fine, Dexter X. Jin, Geoffrey R. Oxnard, Jason Hughes, Zoe June Assaf, Yi Cao, Vladan Antic, Ole Gjoerup, Amanda Young, David Fabrizio, Shaily Lakhanpal, Richard Zuniga, Katja Schulze, Lincoln W. Pasquina. Validation of a tumor-naïve circulating tumor DNA (ctDNA) response monitoring panel in advanced non-small cell lung cancer (aNSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 971.

Classification of multi-feature fusion ultrasound images of breast tumor within category 4 using convolutional neural networks.

Breast tumor is a fatal threat to the health of women. Ultrasound (US) is a common and economical method for the diagnosis of breast cancer. Breast imaging reporting and data system (BI-RADS) category 4 has the highest false-positive value of about 30% among five categories. The classification task in BI-RADS category 4 is challenging and has not been fully studied. This work aimed to use convolutional neural networks (CNNs) for breast tumor classification using B-mode images in category 4 to overcome the dependence on operator and artifacts. Additionally, this work intends to take full advantage of morphological and textural features in breast tumor US images to improve classification accuracy. First, original US images coming directly from the hospital were cropped and resized. In 1385 B-mode US BI-RADS category 4 images, the biopsy eliminated 503 samples of benign tumor and left 882 of malignant. Then, K-means clustering algorithm and entropy of sliding windows of US images were conducted. Considering the diversity of different characteristic information of malignant and benign represented by original B-mode images, K-means clustering images and entropy images, they are fused in a three-channel form multi-feature fusion images dataset. The training, validation, and test sets are 969, 277, and 139. With transfer learning, 11 CNN models including DenseNet and ResNet were investigated. Finally, by comparing accuracy, precision, recall, F1-score, and area under curve (AUC) of the results, models which had better performance were selected. The normality of data was assessed by Shapiro-Wilk test. DeLong test and independent t-test were used to evaluate the significant difference of AUC and other values. False discovery rate was utilized to ultimately evaluate the advantages of CNN with highest evaluation metrics. In addition, the study of anti-log compression was conducted but no improvement has shown in CNNs classification results. With multi-feature fusion images, DenseNet121 has highest accuracy of 80.22±1.45% compared to other CNNs, precision of 77.97±2.89% and AUC of 0.82±0.01. Multi-feature fusion improved accuracy of DenseNet121 by 1.87% from classification of original B-mode images (p<0.05). The CNNs with multi-feature fusion show a good potential of reducing the false-positive rate within category 4. The work illustrated that CNNs and fusion images have the potential to reduce false-positive rate in breast tumor within US BI-RADS category 4, and make the diagnosis of category 4 breast tumors to be more accurate and precise.

Measurement and Comparison of Kidney Dimension Using Ultrasonography and Computed Tomography Scan

Background and Objective: The measurement of renal size is of utmost importance in patients with renal failure, especially azotemia. The present study aimed to assess and compare kidney size using ultrasonography and computed tomography (CT) scans. Materials and Methods: This cross-sectional study was conducted on subjects over 18 years of age who were referred to Hajar Hospital of Shahrekord for an ultrasound and CT scan of the kidney or abdomen with a time interval of less than one week. Kidney dimensions were measured by transabdominal ultrasound model Mindray DC-60Exp with 2-5 MHz probe in supine position and oblique if needed. After less than a week, the dimensions of the kidneys were determined by a Siemens 16-slice CT scan machine (with reconstruction at different stages). Data were analyzed in SPSS software using statistical tests. Results: Right and left kidney dimensions using ultrasonography were obtained at 103.46±13.53 and 107.44±12.15 mm, respectively. These dimensions were 103.10±15.76 and 108.81±14.77 mm, respectively, using a CT scan. The agreement between the ultrasonography and CT scan in determining right and left kidney dimensions was higher than 95% in 96.7% and 95% of all cases, respectively. Sensitivity, specificity, false positive value, negative-positive value, positive predictive value, and negative predictive value for right kidneys were reported as 82.4%, 92.6%, 7.4%, 17.6%, 73.7%, and 95.4% using ultrasonography. These values for the left kidney were 88, 88.6, 11.4, 11, 45.8, and 98.5%, respectively. Conclusion: Regarding the importance of kidney size in determining renal failure, it seems that in some cases, reliance on ultrasonography is unclear, and it is recommended that kidney size be determined using a CT scan in patients with suspected kidney failure

A novel sensitive DDoS attacks against statistical test in network traffic fusion

AbstractDistributed denial of service (DDoS) attacks are common attacks that limit casual users from cracking critical network resources. A method to find the DDoS attack comes to light as an essential and hot research issue. Therefore, the increment in network usage scale demands more challenges to see the DDoS attack. Non‐real‐time collection of network traffic will make sincere complications inaccuracy; DDoS attack detection, and efficiency. Lately, sketch data structures play an excellent role in high‐speed networks to suppress and combine network traffic issues. However, the sketch data structure has issues rebuilding the reverse keys that behave abnormally due to the reversible hash function. To rectify the above difficulties, we include the Chinese Remainder Theorem‐based Reverse Sketching (CRTRS). However, Reverse Sketching is not good at suppressing and combining heavy network traffic, but it can easily detect abnormal keys. According to the network traffic records setup by CRTRS, we add the Modified Cumulative Sum Quality Control Scheme (MCSQCS) that encourages DDoS attack detection. The enforcement of the proposed work with DDoS attack detection has two kind of different data sets. The result of the experiments shows that it gives more accuracy and efficiency in detecting DDoS attacks. Therefore, comparing other DDoS detecting methods with the sketch data structure has fewer computational complications. Improving the anomalous keys is the most critical challenge in this proposed work. To illustrate the accuracy rate, we experiment with a new technique called CIC‐Friday Heavy network traffic fragment. We also explain the experimental results of the MAWI heavy network fragment with the various parameter setups. To compare our proposed method with other similar existing methods, we use a sketch data structure to manage the network traffic data. It also uses the MSPCA method to detect the DDoS attack. The information‐based analysis helps to see the DDoS attack. The result analysis of True Positive Value (TPV) and False Positive Value (FPV) with MSPCA techniques has 79% and .1% accuracy. Similarly, information‐based detection has an accuracy of about 90% and 5%. Hence, the proposed method gives more accuracy for the detection of DDoS attacks.

RETRACTED ARTICLE: An abnormal traffic detection method using GCN-BiLSTM-Attention in the internet of vehicles environment

In-vehicle network intrusion detection tasks, it is usually necessary to simultaneously meet the requirements of low computational power consumption, real-time response, and high detection accuracy. In response to the class imbalance problem in existing vehicle network anomaly flow detection methods, which leads to longer training convergence time and low detection accuracy, an anomaly flow detection method using GCN-BiLSTM-Attention is proposed. Firstly, Graph Convolutional Networks (GCN) is used to obtain spatial correlations between data streams. Secondly, obtaining the time correlation to predict the next time slice flow matrix by capitalizing the variant Bidirectional Long Short-Term Memory (BiLSTM) network. Last but not least, an attention mechanism is designed for extracting key information from the data stream. The results of experiment prove that the binary classification false positive rate, detection rate, and F1 value of the proposed GCN-BiLSTM-Attention-based anomaly flow detection method on the NSL-KDD dataset are 95.87%, 6.31%, and 94.25%, respectively; The false positive rate, detection rate, and F1 value on the CICID2017 dataset are 6.01%, 94.12%,and 94.36%, respectively. The proposed GCN-BiLSTM-Attention model has exceeded the compared methods in detecting abnormal traffic in the context of the Internet of Vehicles, and it can better preserve local features of traffic data.

17th International Conference on Malignant Lymphoma, Palazzo dei Congressi, Lugano, Switzerland, 13 - 17 June, 2023.

Background: Follicular Lymphoma (FL) patients frequently achieve long remissions with frontline chemoimmunotherapy (CIT); nevertheless, a subset of patients will experience early progression and a poor outcome. Fluoro-[18F]-deoxy-2-D-glucose positron emission tomography (PET/CT) and liqBio-MRD performed at the end of CIT induction can identify patients at significantly higher risk of relapse. The aim of this study is to analyze if an interim PET/TC scan could earlier identify high-risk FL. Methods: We retrospectively identified 121 patients grade 1–3A FL patients who had end of treatment (EOT) and interim PET/CT (after 4 cycles of frontline CIT) between 2012 and 2022. PET/CT were analyzed using Deauville score (DS). Interim cell free DNA (cfDNA) was available in 15 patients, so we could measure MRD by NGS (Jiménez-Ubieto 2023). Results: Most patients were treated with R-CHOP (n = 94; 78%) or rituximab-bendamustine (n = 24; 20%). Rituximab maintenance (RM) was used in 87%. Median follow-up was 34 months (3–115). A total of 34 patients (28%) relapsed, 21 (17.5%) within 24 months after CIT (POD24). Histological transformation (HT) rate was 2.5%. The EOT CR rate was 81.5%. PET/CTEOT were predictive of relapse, with 2-years PFS 47% in PET/CTEOT(+) vs. 89% in PET/CTEOT(-) (p < .001).On iPET/CT, 41 (34%) patients were PET/CT(+).The estimated 5 year-PFS at was 72% in patients with a negative iPET/CT versus 29% in those with a positive iPET/CT (p < 0.001) (Figure 1a). POD24 was found in 34% and 7.6% of patients with iPET/CT(+) and (-)respectively, (p < 0.001; HR 7.89). iPET/CT(-) presented a negative predictive value of 94% for POD24. Additionally, in 78 patients with iPET/CT(-) and 93 patients with PET/CTEOT(-) MR was not relevant to predict POD24 (2-year PFS of 93.5%/85.7% and 91.3%/87.5%, respectively) (Figure 1b). Notwithstanding, patients with iPET/CT(+) or EOTPET/CT(+) MR had a clear impact in PFS (p < 0.05). Additionally SUVmax >3,7 in the iPET/CT(-) was predictive for HT, regardless of being iPET/CT(-) (6-year TFS of 81.9% vs. 100%). We found trackable mutations in 14 of 15 patients with interim cfDNA MRD analysis (93%). 4/14 patients relapsed after a median of 8.5 months. 3/4 of the relapsing patients had a MRD interim (+) and all the non-relapsing patients a negative MRD test (p < .001). Interestingly, of the 10 non-relapsing patients, 2 have iPET/CT(+) (mesenteric masses). A biopsy excluded lymphoma, confirming the false positive value of the PET/CT. Encore Abstract - previously submitted to EHA 2023 Keywords: Indolent non-Hodgkin lymphoma, Liquid biopsy, PET-CT No conflicts of interests pertinent to the abstract.

An Improved Machine Learning Algorithm for Silver Nanoparticle Images: A Study on Computational Nano-Materials

Objectives: To study the determination and classification of Scanning Electron Microscopy (SEM) images of silver nanoparticles using digital image processing techniques and classifying the images using various machine learning classifiers namely; SVM, K-NN, and PNN classifiers. Methods: Segmentation techniques namely; Fuzzy C-Means (FCM) and K-Means were applied to extract geometric features from SEM images of silver nanoparticles. The size of nanoparticles was determined and classified based on various nano applications. The categorization of silver nanoparticles was done based on the geometrical feature value, i.e. area, and was further classified into three categories such as; 0-100 nm, 101-200 nm, and 201-500 nm using both the segmentation techniques. Further, the proposed method extracts texture features such as; kurtosis, skewness, and entropy and used SVM, K-NN, and PNN classifiers to classify the silver nanoparticle SEM images. The proposed results are analyzed and interpreted by chemical experts to observe the effectiveness of the proposed method. Findings: To improve the classification accuracy of Silver nanoparticle images, textural features such as; kurtosis, skewness, and entropy were extracted and SVM, K-NN, and PNN classifiers are used to classify the silver nanoparticle images. The performance of the proposed algorithm is evaluated based on performance evaluation measures such as; specificity, sensitivity, true predictive value, and false positive value. Higher TPV, specificity, and sensitivity values show promising results. It is observed that the SVM classifier has 75.00%, K-NN has 62.50%, and PNN yielded an accuracy of 93.00%. Novelty: Traditional synthesis and characterization techniques are inefficient and time-consuming and require more space and infrastructure to analyze the size of the nanoparticle. The current study circumvents these limitations. The silver nanoparticle images used in the present study are synthesized using rudanti fruit seeds, and the process is named as green synthesis process or biological method. This method is widely used because it does not use anytoxic chemicals, is inexpensive, andis environmentally friendly. In the previous work, only geometrical features namely; Area (pore size), and range-wise pore size were calculated depending on the application-oriented. The present study employs two phases; the training phase (knowledge base) and the testing phase(classification). The training phase includes segmentation, feature extraction, and categorization depending on the applications. The testing phase extracts the textural features such as; kurtosis, skewness, and entropy, and classifies the silver nanoparticles by using SVM, K-NN, and PNN classifiers. Keywords: Silver nanoparticles; Image segmentation; FCM; KMeans; SVM; KNN; PNN

Abstract TP59: Prehospital Identification Of Acute Ischaemic Stroke With Large Vessel Occlusion

Background: The prehospital stroke scale B-FAST (Gaze deviation, Face paresis, Arm weakness, Visual loss and Speech disruptions ) was implemented in the Emergency Medical Services (EMS) in the Western Norway Regional Health Trust (1,1 million catchment population; eight primary stroke centres (PSC) ; two comprehensive stroke centres (CSC)) in 2019 to detect acute ischaemic stroke (AIS) patients with large vessel occlusion (LVO). Prehospital presence of gaze deviation (B-symptom)and at least one FAST symptom, may lead to PSC bypass and proceed directly to a CSC. The aim of the study was to investigate the sensitivity of the B-FAST scale detecting LVO in the prehospital setting. Methods: Retrospective study including patients with at least one B-FAST symptom at EMS arrival. We obtained patient data from all code red stroke dispatches from Emergency Medical Communication Centres (EMCC) and local endovascular treatment (EVT) registries in the period August-December 2020. Variables collected were clinical features, CT examination and reperfusion treatment. Definition of prehospital positive B-FAST was presence of Gaze deviation (B-symptom)and at least one FAST symptom at EMS arrival. Results: We collected data on 643 patients, 59 were diagnosed with a LVO stroke at hospital arrival. In the LVO group, 7 of 59 patients presented with prehospital positive B-FAST, resulting in a sensitivity of 12 %. EVT was performed in 38/59 (64%) cases. Two patients were false negative and positive predictive value was 78%. Specificity was 99% and negative predictive value 83%. Median transfer delay for patients initially admitted to a PSC before transfer to a CSC, was 130 min (IQR 60-167 min). Conclusion: The use of B-FAST by EMS personnel for identification of AIS patients with LVO has a low sensitivity, but presence of gaze deviation predicts LVO stroke with high certainty.

Colposcopic Findings for Detecting Pre-invasive Lesion of Cervix Among Visually Inspected Acetic Acid Positive Cases

Background: Cervical cancer-a preventable disease, is the second most commonly diagnosed cancer among women of developing countries. Paps smear is the gold standard screening method worldwide. But a low-cost test, visual inspection of cervix with acetic acid (VIA) has been emerged as an alternative for use in lowresource settings. VIA is simple and easy to perform by auxiliary health professionals. Colposcopy is an essential procedure in the evaluation of screen positive cervix though it requires considerable training and experience. Objective: The objective of this study was to evaluate the VIA positive cases by colposcopy, colposcopy guided biopsy and histopathological examination. Methods: This was a cross sectional observational study conducted among 100 VIA positive cases attended at GOPD of Dhaka Medical College Hospital from July 2014 to December 2014. Colposcopy was performed in all VIA positive cases. Subsequent biopsy and histopathological examinations were done for those with colposcopic abnormal findings. Results: The mean age of the study population was 37.9±9.3 years. Among VIA positive cases 66.0% had complaints of whitish vaginal discharge. Among the study population, colposcopically 46.0% had healthy cervix (normal) and 54.0% had abnormal findings. Colposcopy guided biopsy was taken from those with abnormal findings. Histopathological findings were normal in 38.9% cases, chronic cervicitis in 20.37% cases, squamous cell metaplasia in 3.7% cases, CIN I in 16.7%, CIN II in11.11%, CIN III in 5.6% and 3.7% cases were carcinoma cervix. True positive value was 20, false positive value 34 and positive predictive value of colposcopy was 37.03%. Conclusion: This study concluded that VIA is effective in detection of CIN and invasive carcinoma of cervix. It also suggests the role of colposcopy in the evaluation of CIN and other cervical diseases in screen positive cases. VIA can be used as a screening tool in low and middle income countries like Bangladesh, not only in rural areas and small health centres, but also in hospitals, cancer institutes, and other health facilities with better resources. Bangladesh Med Res Counc Bull 2022; 48(1): 27-32

The role of magnetic resonance imaging in assessing the extent of tongue squamous cell carcinoma: A prospective cohort study

PurposeTo assess the false-positive and false-negative MRI results in evaluating the extent of tongue squamous cell carcinoma. MethodsA prospective cohort series of 165 patients was enrolled to assess the false-positive and false-negative MRI results in evaluating the extent of tongue squamous cell carcinoma by comparing intraoperative tumor profile images and postoperative pathological sections. The differences between two-dimensional tumor margins were analyzed using Mimics 15.0 and Geomagic Control 16.0. A paired-samples t-test was used to analyze the agreement among MRI, intraoperative and pathological findings regarding the extent of tongue tumors. Multiple linear regression analysis was used to analyze associated factors. ResultsThe mean and maximum false-positive values of pathological specimens was 1.95±1.39 mm (95% limit of agreement (LoA) 1.70–2.14) and 3.21 mm, respectively; the false-negative value was 0.44±0.49 mm. The false-positive value of intraoperative specimens was 1.52±0.87 mm (95% LoA 1.36–1.64); the false-negative value was 0.35±0.20 mm. Tumor morphology (ulcer type) (p<0.01) and depth of invasion (DOI) (≤5 mm) (p<0.01) were significantly correlated with the false-positive values of intraoperative and pathology specimens. ConclusionThe false-positive values are important when judging the invasion margin of tongue cancer and forming MRI-based operative plans; the false-negative value was almost negligible.

Machine Learning Based Hardware Trojan Detection Using Electromagnetic Emanation

The growing threat of Hardware Trojans (HT) in the System-on-Chips (SoC) industry has given way to the embedded systems researchers to propose a series of detection methodologies to identify and detect the presence of Trojan circuits or logics inside a host design in the various stages of the chip design and manufacturing process. Many state of the art works propose different techniques for HT detection among which the popular choice remains the Side-Channel Analysis (SCA) based methods that perform differential analysis targeting the difference in consumption of power, change in electromagnetic emanation or the delay in propagation of logic in various paths of the circuit. Even though the effectiveness of these methods are well established, the evaluation is carried out on simplistic models such as AES coprocessors and the analytical approaches used for these methods are limited by some statistical metrics such as direct comparison of EM traces or the T-test coefficients. In this paper, we propose two new detection methodologies based on Machine Learning algorithms. The first method consists in applying the supervised Machine Learning (ML) algorithms on raw EM traces for the classification and detection of HT. It offers a detection rate close to 90% and false negative smaller than 5%. In the second method, we propose an outlier/novelty algorithms based approach. This method combined with the T-test based signal processing technique, when compared with state-of-the-art, offers a better performance with a detection rate close to 100% and a false positive smaller than 1%. In different experiments, the false negative is nearly the same level than the false positive and for that reason the authors only show the false positive value on the results. We have evaluated the performance of our method on a complex target design: RISC-V generic processor. Three HTs with their corresponding sizes: 0.53%, 0.27% and 0.09% of the RISC-V processors are inserted for the experimentation. In this paper we provide elaborative details of our tests and experimental process for reproducibility. The experimental results show that the inserted HTs, though minimalistic, can be successfully detected using our new methodology.

Enhancement detection distributed denial of service attacks using hybrid n-gram techniques

Distributed denial of service (DDoS) attacks have always been a concern of cyber experts. To detect DDoS attacks, several methods can be used. One of the methods used in this research is the n-gram technique. The n-gram approach analyzes the payload of data packets that enter the network to obtain attack patterns. Data is captured and analyzed, after which it is compared with clean data packets. The chi-square distance value close to 1 indicates that the two packages are very similar so that the data packet is not an attack. A deal less than one means the data packet is categorized as an attack. In this research, the threshold for determining the attack level can be lowered to obtain a very high detection accuracy. As a result, the 2-gram technique has a detection accuracy rate with the lowest false positive value of around 13%, with the highest actual positive ratio reaching 99.98%.

Evaluation of the adherence to antidiabetic medications among Iraqi patients with T2DM using the Iraqi antidiabetic medication adherence scale (IADMAS)

Background and Aims: Management of diabetes is a multifactorial process, and adherence to treatment programs plays a role in glycemic control. The present study evaluates the adherence rate to the antidiabetic medications with a newly developed scale and patients’ HbA1c levels among Iraqi patients with type-2 diabetes. Methods: A cross-sectional study among adult patients receiving diabetic care in public and private healthcare settings within Baghdad City was conducted. The medication adherence was evaluated using the Iraqi Antidiabetic Medication Adherence Scale (IADMAS) to determine the factors associated with non-adherence to anti-diabetic medications. Glycated hemoglobin (HbA1c) levels of the participants were used as an indicator of glycemic status. Results: The outcome measures include the comparison between glycemic status and the claims of antidiabetic medication adherence of patients with T2DM, in addition to the expected benefits to clinical practice. A total of 442 patients with type 2 diabetes participated in the study and responded by completing the given questionnaire (response rate: 96.1%). The prevalence of non-adherence to medication was reported to be no more than 30%. Adherence based on HbA1c values reflected a high false-positive value of adherence with an extremely high true negative value. The adherence of Iraqi patients to their medications demonstrated a high negative predictive value (0.905) and a high sensitivity (0.928) with low specificity (0.156) values. Conclusion: The use of IADMAS in this study failed to confirm the consistency between the apparent claims of adherence to anti-diabetic medications and the HbA1c value as a marker of glycemic control.

Enhancement Detection DDoS Attacks Using Hybrid N-Gram Techniques

DDoS attacks have always been a concern of cyber experts. To detect DDoS attacks, there are several methods that can be used. One of the methods used in this research is the N-Gram technique. The n-Gram technique analyzes the payload of data packets that enter the network to obtain attack patterns, data is captured and analyzed after which it is compared with net data packets. The chi-square distance value that is close to 1 indicates that the two packages are very similar, so that the data packet is not an attack. A value less than 1 means the data packet is categorized as an attack. In this research, the threshold for determining the level of attack can be lowered to obtain a very high detection accuracy. As a result, the 2-Gram technique has a detection accuracy rate with the lowest false positive value of around 13% with the highest true positive ratio reaching a value of 99.98%.

Performance Study Of Uncertainty Based Feature Selection Method On Detection Of Chronic Kidney Disease With SVM Classification

Chronic Kidney Disease (CKD) is a disorder that impairs kidney function. Early signs of CKD patients are very difficult until they lose 25% of their kidney function. Therefore, early detection and effective treatment are needed to reduce the mortality rate of CKD sufferers. In this study, the authors diagnose the CKD dataset using the Support Vector Machine (SVM) classification method to obtain accurate diagnostic results. The authors propose a comparison of the result on applying the feature selec- tion method to get the best feature candidates in improving the classification result. The testing process compares the Symmetrical Uncertainty (SU) and Multivariate Symmetrical Uncertainty (MSU) feature selection method and the SVM method as a classification method. Several experimental scenarios were carried out using the SU and MSU feature selection methods using the CKD dataset. From the results of the tests carried out, it shows that using the MSU feature selection method with 80%: 20% data split produces nine important features with an accuracy value of 0.9, sensi- tivity 0.84, specification 1.0, and when viewed on the ROC graph, the MSU method graph shows the true positive value is higher than the false positive value. So the classification using the MSU feature selection method is better than the SU feature selection method by 90% accuracy

Gas Chromatographic Fingerprint Analysis for the Comparison of Seized Cannabis Samples.

Cannabis sativa L. is widely used as recreational illegal drugs. Illicit Cannabis profiling, comparing seized samples, is challenging due to natural Cannabis heterogeneity. The aim of this study was to use GC–FID and GC–MS herbal fingerprints for intra (within)- and inter (between)-location variability evaluation. This study focused on finding an acceptable threshold to link seized samples. Through Pearson correlation-coefficient calculations between intra-location samples, ‘linked’ thresholds were derived using 95% and 99% confidence limits. False negative (FN) and false positive (FP) error rate calculations, aiming at obtaining the lowest possible FP value, were performed for different data pre-treatments. Fingerprint-alignment parameters were optimized using Automated Correlation-Optimized Warping (ACOW) or Design of Experiments (DoE), which presented similar results. Hence, ACOW data, as reference, showed 54% and 65% FP values (95 and 99% confidence, respectively). An additional fourth root normalization pre-treatment provided the best results for both the GC–FID and GC–MS datasets. For GC–FID, which showed the best improved FP error rate, 54 and 65% FP for the reference data decreased to 24 and 32%, respectively, after fourth root transformation. Cross-validation showed FP values similar as the entire calibration set, indicating the representativeness of the thresholds. A noteworthy improvement in discrimination between seized Cannabis samples could be concluded.