False Negatives Research Articles

Genetics healthcare providers' experiences counseling patients with results from consumer genomic testing.

Consumer genomic testing (CGT), including direct-to-consumer and consumer-initiated testing, is increasingly widespread yet has limited regulatory oversight. To assess the current state, we surveyed genetics healthcare providers' experiences with CGT. A retrospective survey about experiences counseling on CGT results was completed by 139 respondents recruited from the National Society of Genetic Counselors, Clinical Cancer Genomics Community of Practice, and genetics professional societies. Among respondents, 41% disagreed with the statement that potential benefits of CGT outweigh harms, 21% agreed, and 38% were undecided. A total of 94% encountered ≥1 challenge counseling CGT patients, including adverse psychosocial events (76%), incorrect variant interpretation (68%), and unconfirmed results (69%); unconfirmed results were more common among oncology providers (p = 0.03). Providers reporting higher total challenge scores (p = 0.004) or more psychosocial or interpretation challenges (p ≤ 0.01) were more likely to indicate CGT harms outweigh benefits. Those with higher CGT clinical volume were more likely to indicate benefits outweigh harms (p = 0.003). Additional CGT challenges included patient understanding and communication of results, false negatives, incorrect testing/care, and financial costs; seven respondents (6%) documented positive outcomes. Providers counseling CGT patients encounter psychosocial and medical challenges. Collaborations between regulators, CGT laboratories, providers, and consumers may help mitigate risks.

Optimizing credit card fraud detection: a deep learning approach to imbalanced datasets

Imbalanced datasets pose a significant challenge in credit card fraud detection, hindering the training effectiveness of models due to the scarcity of fraudulent cases. This study addresses the critical problem of data imbalance through an in-depth exploration of techniques, including cross-entropy loss minimization, weighted optimization, and synthetic minority oversampling technique-based resampling, coupled with deep neural networks (DNNs). The urgent need to combat class imbalances in credit card fraud datasets is underscored, emphasizing the creation of reliable detection models. The research method delves into the application of DNNs, strategically optimizing and resampling the dataset to enhance model performance. The study employs a dataset from October 2018, containing 284,807 transactions, with a mere 492 classified as fraudulent. Various resampling techniques, such as undersampling and SMOTE oversampling, are evaluated alongside weighted optimization. The results showcase the effectiveness of SMOTE oversampling, achieving an accuracy of 99.83% without any false negatives. The study concludes by advocating for flexible strategies, integrating cutting-edge machine learning methods, and developing adaptive defenses to safeguard against emerging financial risks in credit card fraud detection.

Clinical Evaluation of a New Chemiluminescence Assay for the Detection of Treponema Pallidum Antibodies.

A new chemiluminescence assay, the Anti-TP-Ⅱ assay, is going to be commercially available in clinical laboratories in China and other countries. This study examined the performance of the new assay for the detection of TP infection and compared it with that of the Anti-TP assay by using large amounts of clinical samples. The precision, accuracy, anti-interference ability, and the clinical sensitivity and specificity of the Anti-TP-Ⅱ assay were evaluated. In addition, compared with those of the Anti-TP assay, the false positive and false negative rates of the Anti-TP-Ⅱ assay were evaluated for 2,436 clinical routine samples and 711 preselected Anti-TP assay reactive samples. Discrepancy of the samples was investigated with the recomLinec Treponema IgM/IgG kit or the Elecsys syphilis assay. The precision, accuracy, and anti-interference ability of the Anti-TP-Ⅱ assay met the national standard of China, and there was an overall agreement of 96.75% (Kappa = 0.91) between the two assays. The sensitivity and specificity of the Anti-TP-Ⅱ assay were 100% (95% CI: 94.13% to 100%) and 99.92% (95% CI: 99.70% to 99.99%), respectively. Compared with the Anti-TP assay, the Anti-TP-Ⅱ assay significantly reduced the number of borderline samples and the false positive rate. Considering its excellent performance, the Anti-TP-Ⅱ assay is a good screening test for high-throughput laboratories and can replace the previous generation of reagents, the Anti-TP assay, with a superior specificity.

Expanding molecular diagnostic coverage for tuberculosis by combining computer-aided chest radiography and sputum specimen pooling: a modeling study from four high-burden countries

BackgroundIn 2022, fewer than half of persons with tuberculosis (TB) had access to molecular diagnostic tests for TB due to their high costs. Studies have found that the use of artificial intelligence (AI) software for chest X-ray (CXR) interpretation and sputum specimen pooling can each reduce the cost of testing. We modeled the combination of both strategies to estimate potential savings in consumables that could be used to expand access to molecular diagnostics.MethodsWe obtained Xpert testing and positivity data segmented into deciles by AI probability scores for TB from the community- and healthcare facility-based active case finding conducted in Bangladesh, Nigeria, Viet Nam, and Zambia. AI scores in the model were based on CAD4TB version 7 (Zambia) and qXR (all other countries). We modeled four ordinal screening and testing approaches involving AI-aided CXR interpretation to indicate individual and pooled testing. Setting a false negative rate of 5%, for each approach we calculated additional and cumulative savings over the baseline of universal Xpert testing, as well as the theoretical expansion in diagnostic coverage.ResultsIn each country, the optimal screening and testing approach was to use AI to rule out testing in deciles with low AI scores and to guide pooled vs individual testing in persons with moderate and high AI scores, respectively. This approach yielded cumulative savings in Xpert tests over baseline ranging from 50.8% in Zambia to 57.5% in Nigeria and 61.5% in Bangladesh and Viet Nam. Using these savings, diagnostic coverage theoretically could be expanded by 34% to 160% across the different approaches and countries.ConclusionsUsing AI software data generated during CXR interpretation to inform a differentiated pooled testing strategy may optimize TB diagnostic test use, and could extend molecular tests to more people who need them. The optimal AI thresholds and pooled testing strategy varied across countries, which suggests that bespoke screening and testing approaches may be needed for differing populations and settings.

Multi-frame temporal dense nested attention method for detecting GEO objects

The geostationary orbit is a special and important spatial orbital resource. However, space resident objects in this orbit pose potential threats to space security, characterized as dim and small targets in imaging features. To solve the problem of detecting dim and small targets in geostationary orbit, we propose a multi-frame temporal dense nested attention method, which consists of a uniform linear motion constraint module and a dense nested attention module. The dense nested attention module adopts a multi-layer U-shaped structure and channel spatial attention mechanism, which can preserve the deep features of small targets, extract and fuse features from different scales of feature maps, and finally obtain the preliminary detection results of single-frame images. The uniform linear motion constraint module uses the prior information of the target motion in the image sequence, removes false positives and supplements false negatives, and improves the detection performance. Experiments were conducted on the SpotGEO dataset publicly released by the European Space Agency. The results of our experiments indicate that our method outperforms the state-of-the-art approach, showing a 12.83% reduction in Mean Squared Error compared to the current leading method. Our method, characterized by its minimal parameter requirement and independence from extensive dataset pre-training, is ideally suited for integration into small satellite devices, enabling efficient monitoring and mitigation of resident space objects.

False Negative Rate of Sentinel Lymph Node Biopsy (SLNB) in Breast Cancer Patients after Neoadjuvant Chemotherapy

Background: Managing the axilla in patients with node-positive breast cancer who converted to node-negative patients after neoadjuvant chemotherapy is a challenging issue. We aim to analyze the false negative rate (FNR) of SLNB after neoadjuvant chemotherapy in this group of patients. Methods: In this cross-sectional study, we reviewed the results of SLNB and ALND in 368 breast cancer patients who underwent neoadjuvant chemotherapy from 2015 to 2019. The study included patients with pathologically proven axillary lymph nodes undergoing NAC. We collected the data from the Breast Diseases Research Center of Tehran University of Medical Sciences. Results: The average age of the patients was 46.58±10.91. Of all the patients, 205 (55.7%) had positive SLN in the histologic analysis after surgery, while 163 (44.3%) had negative results. The study also revealed that the FNR of SLNB was 9.8% (n=16). Our results showed that SLN had 86.55% sensitivity and 100% specificity in detecting involved nodes. Furthermore, after multivariable analysis, we observed that a higher number of ALND dissections were associated with a higher FNR (Odds ratio; OR=1.20, 95% CI: 1.02-1.42); while a higher number of SLN excisions was linked with a lower FNR (OR=0.41; 95% CI: 0.17-0.98). Conclusion: After NAC in breast cancer patients with positive lymph nodes, SLNB is feasible with a low FNR; the latter is correlated with the number of nodes removed during the procedure.

Intraoperative Nodal Palpation is a Mandatory Component of Sentinel Lymph Node Biopsy for Breast Cancer

Background: In the era of Z-0011, it is mandatory to decrease not only the false negative rate (FNR) of sentinel lymph node (SLN) biopsy but also the risk of residual metastatic nodes after SLN biopsy. Method: SLN biopsy with intraoperative nodal palpation (INP) was performed in patients with clinically node-negative (cN0) breast cancer. All identified blue and hot nodes were removed as blue/hot SLNs, and any suspicious palpable nodes were removed as palpable SLNs. Nodes that were incidentally removed with neighboring the blue/hot SLNs were classified as para-SLNs. Patients with positive SLNs on frozen section underwent axillary lymph node dissection (ALND) except for patients who met the Z-0011 and AMAROS criteria for exemption. Results: Palpable SLNs and para-SLNs were identified in 202 patients. Of 200 patients, excluding 2 patients only with palpable SLNs, 46 patients had involvements of blue/hot SLNs, and 14 had palpable and para-SLNs harboring additional metastasis. When false negative rate (FNR) was calculated based on blue/hot SLNs and palpable SLNs, the additional use of INP resulted in a FNR of 45.2%. Subsequently, ALND was performed in 43 patients with positive blue/hot or palpable SLNs. Residual nodal involvement was found in 28 (65%) of 43 patients after removing blue/hot SLNs. However, after removing palpable SLNs, the rate of residual nodal metastases significantly decreased from 65% (28/43) to 36% (13/36) (p=0.0133). Conclusion: INP decreased both the FNR of SLN biopsy and the risk of residual metastatic nodes after SLN biopsy.

The utility of intraoperative marrow margin frozen section in extremity bone sarcoma resection.

Intraoperative frozen section analysis is commonly used to evaluate marrow margins during extremity bone sarcoma resections, but its efficacy in the era of magnetic resonance imaging is debated. This study aimed to compare the accuracy of intraoperative frozen section assessment with final pathology, assess its correlation with gross intraoperative margin assessment, and evaluate its impact on surgical decision making. Consecutive patients undergoing extremity bone sarcoma resections from 2010 to 2022 at a single sarcoma center were included. Intraoperative frozen section and gross margin assessments were compared to final pathology using positive predictive values (PPV) and negative predictive values (NPV). Changes in surgical decisions due to positive intraoperative margins were recorded. Of 166 intraoperative frozen section marrow margins, four were indeterminant/positive, with two false positive/indeterminant findings and two false negatives compared to final pathology. Gross intraoperative assessment had no false positives and two false negatives. Frozen section analysis yielded a PPV of 50% (95% confidence interval [CI]: 16%-84%) and NPV of 98.8% (95% CI: 97%-100%), while gross assessment had a PPV of 100% (95% CI: 16%-100%) and NPV of 98.8% (95% CI: 97%-100%). Positive frozen section margins led to additional resections in three of four cases. Intraoperative frozen section analysis did not offer added clinical value beyond gross margin assessment in extremity bone sarcoma resections. It exhibited a low PPV and led to unnecessary additional resections. Gross intraoperative assessment proved adequate for margin evaluation, potentially saving time and resources.

An enhanced algorithm for semantic-based feature reduction in spam filtering.

With the advent and improvement of ontological dictionaries (WordNet, Babelnet), the use of synsets-based text representations is gaining popularity in classification tasks. More recently, ontological dictionaries were used for reducing dimensionality in this kind of representation (e.g., Semantic Dimensionality Reduction System (SDRS) (Vélez de Mendizabal et al., 2020)). These approaches are based on the combination of semantically related columns by taking advantage of semantic information extracted from ontological dictionaries. Their main advantage is that they not only eliminate features but can also combine them, minimizing (low-loss) or avoiding (lossless) the loss of information. The most recent (and accurate) techniques included in this group are based on using evolutionary algorithms to find how many features can be grouped to reduce false positive (FP) and false negative (FN) errors obtained. The main limitation of these evolutionary-based schemes is the computational requirements derived from the use of optimization algorithms. The contribution of this study is a new lossless feature reduction scheme exploiting information from ontological dictionaries, which achieves slightly better accuracy (specially in FP errors) than optimization-based approaches but using far fewer computational resources. Instead of using computationally expensive evolutionary algorithms, our proposal determines whether two columns (synsets) can be combined by observing whether the instances included in a dataset (e.g., training dataset) containing these synsets are mostly of the same class. The study includes experiments using three datasets and a detailed comparison with two previous optimization-based approaches.

The surgical technique and protocol for dynamic sentinel node biopsy for penile cancer at a Southeast Asian regional hospital.

Lymph node status is a key prognostic factor in penile cancer. The European Association of Urology (EAU) recommends intermediate-risk (pT1a, Grade 2) or high-risk (pT1b or greater) penile cancer patients with clinically non-palpable inguinal lymph node (cN0) to undergo either an invasive bilateral modified inguinal lymph node dissection (ILND) or dynamic sentinel node biopsy (DSNB). DSNB has been reported to have acceptable false negative rates, and lower rates of long-term morbidity compared to ILND. We developed a protocol for DSNB at a regional hospital in Singapore that was adopted from St James's University Hospital, Leeds Teaching Hospitals Trust. Four patients with cN0 penile cancer underwent DSNB between November 2021 and October 2022 according to this protocol. Our surgical technique and protocol are described. The patients' oncological characteristics and their outcomes were evaluated. In this small case series, there was no complication attributable to the performance of DSNB, and there was no groin that was documented to be false negative over a median follow up of 15.5 months (range, 12 to 22 months). Using our protocol, 5 of 8 groins (62.5%) were able to avoid ILND in the cN0 setting. We recommend the adoption of DSNB for the surgical staging of inguinal lymph nodes for patients with intermediate to high-risk penile cancer and non-palpable inguinal nodes due to its significantly lower risks of long-term morbidity compared to ILND. Appropriate specialist training and a multi-disciplinary team is vital to ensure the success of the procedure.

ESR Essentials: diagnostic work-up in patients with symptomatic breast disease-practice recommendations by the European Society of Breast Imaging.

Breast complaints are frequent reasons for consultations in primary care or breast clinics. Breast pain, breast lumps, and nipple discharge are the most common complaints. Less common symptoms such as skin changes and axillary abnormalities also require specific diagnostic approaches. Imaging the symptomatic breast should be performed by appropriately trained breast radiologists following the best practice guidelines and quality standards. Full-field digital mammography (FFDM), digital breast tomosynthesis (DBT), and breast ultrasound (US) are the main modalities used in this primary setting. The choice depends on the patient's age and symptoms. Women younger than 30-years-old are first imaged byUS, whereas women over 40-years-old usually require both FFDM or DBT and US. For women between 30-years-old and 40-years-old, the US is the modality of choice, whereas FFDM or DBT might also be performed if needed. Pregnant or lactating women with palpable lesions or nipple discharge are imaged with US as the first method; FFDM or DBT can also be performed depending on the degree of suspicion as the dose to the fetus is minimal, and shielding may even further reduce the dose. More advanced techniques such as breast magnetic resonance imaging or contrast-enhanced mammography are not indicated in this first diagnostic setting and are reserved for cases of established malignancy (local staging) or rare cases of equivocal findings not otherwise resolved or inflammatory breast cancer. Last, but not least, male breast symptoms should also be addressed with US and/or FFDM. CLINICAL RELEVANCE STATEMENT: It is equally important to correctly diagnose an underlying malignancy and to avoid false positives that would lead to unnecessary biopsies, increased costs, and anxiety for the patient. Proper use of imaging modalities ensures optimal diagnostic approach and minimizes false negatives. KEY POINTS: Ultrasound, full-field digital mammography, or digital breast tomosynthesis are the main imaging modalities in the diagnostic setting, while MRI or contrast-enhanced mammography should be reserved to selected cases. Initial imaging modality includes ultrasound combined with mammography or digital breast tomosynthesis depending on women's age and the presence (or not) of inconclusive findings. A negative imaging evaluation should not deter biopsy when a highly suspicious finding is found on physical examination.

Artificial Intelligence to Predict the Risk of Lymph Node Metastasis in T2 Colorectal Cancer.

To develop and externally validate an updated artificial intelligence (AI) prediction system for stratifying the risk of lymph node metastasis (LNM) in T2 colorectal cancer (CRC). Recent technical advances allow complete local excision of T2 CRC, traditionally treated with surgical resection. Yet, the widespread adoption of this approach is hampered by the inability to stratify the risk of LNM. Data from pT2 CRC patients undergoing surgical resection between April 2000 and May 2022 at one Japanese and one Italian center were analyzed. Primary goal was AI system development for accurate LNM prediction. Predictors encompassed seven variables: age, sex, tumor size and location, lympho-vascular invasion, histological differentiation, and carcinoembryonic antigen level. The tool's discriminating power was assessed via Area Under the Curve (AUC), sensitivity, and specificity. Out of 735 initial patients, 692 were eligible. Training and validation cohorts comprised of 492 and 200 patients, respectively. The AI model displayed an AUC of 0.75 in the combined validation dataset. Sensitivity for LNM prediction was 97.8% and specificity was 15.6%. The Positive and the Negative Predictive Value were 25.7% and 96% respectively. The False Negative (FN) rate was 2.2%, the False Positive was 84.4%. Our AI model, based on easily accessible clinical and pathological variables, moderately predicts LNM in T2 CRC. However, the risk of FN needs to be considered. The training of the model including more patients across Western and Eastern centers -differentiating between colon and rectal cancers- may improve its performance and accuracy.

Deep learning in high-frequency trading: Conceptual challenges and solutions for real-time fraud detection

High-frequency trading (HFT) has transformed financial markets by enabling rapid execution of trades, exploiting market inefficiencies, and optimizing trading strategies. However, this speed and complexity also present significant challenges for real-time fraud detection. Deep learning, a subset of machine learning, offers promising solutions to these challenges through its ability to analyze large volumes of data and uncover intricate patterns. This review explores the conceptual challenges and solutions associated with deploying deep learning for fraud detection in HFT environments. One of the primary challenges in implementing deep learning for HFT fraud detection is the sheer volume and velocity of data. HFT systems generate vast amounts of transactional data in milliseconds, necessitating highly efficient and scalable deep learning models. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are particularly suited for this task due to their ability to process and analyze sequential data efficiently. However, these models require substantial computational resources and sophisticated infrastructure to operate in real time. Another significant challenge is the need for high accuracy and low latency in fraud detection. False positives can lead to unnecessary interventions, while false negatives can result in undetected fraudulent activities. Deep learning models must be fine-tuned to balance these risks, employing techniques such as hyperparameter optimization and ensemble learning to enhance their predictive capabilities. Additionally, integrating real-time anomaly detection methods can help identify suspicious activities promptly, reducing the window of opportunity for fraudsters. Data quality and integrity also pose substantial challenges. HFT environments are susceptible to noise and outliers, which can distort model predictions. Ensuring high-quality data through rigorous preprocessing and anomaly filtering is crucial for the accuracy of deep learning models. Techniques such as data augmentation and normalization can further improve model robustness. To address these challenges, a hybrid approach combining deep learning with traditional statistical methods and rule-based systems can be effective. This approach leverages the strengths of each method, providing a comprehensive fraud detection framework that is both accurate and responsive. Additionally, ongoing model retraining and adaptation to evolving fraud patterns are essential to maintain the effectiveness of the system. In conclusion, while deep learning presents significant opportunities for enhancing real-time fraud detection in high-frequency trading, it also requires addressing challenges related to data volume, computational demands, accuracy, and data quality. By employing a hybrid approach and continually refining models, financial institutions can effectively mitigate fraud risks and safeguard their trading operations.

Left Testicular Torsion in an Undescended Left Testis in a 5-Year-Old Boy: A Case Report

Purpose: Testicular torsion is a urological emergency characterized by the twisting of the spermatic cord structures, and subsequent loss of blood supply to the ipsilateral testis leading to testicular ischemia and infarction. This condition, if not promptly treated, can result in permanent testicular damage. Within 6 hours from onset of testicular pain, testicular exploration is recommended.While testicular torsion is common in normally descended testes, its occurrence in an undescended testis is rare and poses unique diagnostic and management challenges. Methodology: A 5-year-old male presented with a 6-hour history of severe left groin pain. Clinical examination revealed an empty left Hemiscrotum and a tender left groin swelling. Doppler ultrasound confirmed the absence of the left testis in the scrotum, locating it in the left groin with markedly reduced blood supply, suggestive of testicular torsion. Laboratory investigations, including a complete blood count, were within normal limits. Urgent surgical intervention involved detorsion and bilateral orchidopexy, leading to complete resolution of symptoms and an uneventful recovery. Findings: This case highlights the diagnostic difficulties associated with testicular torsion in an undescended testis. The rarity of this condition necessitates heightened clinical suspicion in patients with cryptorchidism presenting with acute abdominal or groin pain. Relevant literature and case reports emphasize the importance of early diagnosis and treatment to salvage the affected testis. Doppler ultrasonography is the diagnostic modality of choice, though clinical suspicion should guide management due to the possibility of false negatives. Surgical intervention remains the definitive treatment, with timely detorsion and orchidopexy being crucial for favorable outcomes. Unique Contribution to Theory, Practice and Policy: Testicular torsion in an undescended testis requires prompt recognition and multidisciplinary management to prevent long-term complications such as infertility. Clinicians should maintain a high index of suspicion for this condition in pediatric patients presenting with inguinal swelling and ipsilateral empty hemiscrotum. Early surgical intervention is essential for the preservation of testicular function and favorable patient outcomes.

Application of a novel nested ensemble algorithm in predicting motor function recovery in patients with traumatic cervical spinal cord injury

Traumatic cervical spinal cord injury (TCSCI) often causes varying degrees of motor dysfunction, common assessed by the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI), in association with the American Spinal Injury Association (ASIA) Impairment Scale. Accurate prediction of motor function recovery is extremely important for formulating effective diagnosis, therapeutic and rehabilitation programs. The aim of this study is to investigate the validity of a novel nested ensemble algorithm that uses the very early ASIA motor score (AMS) of ISNCSCI examination to predict motor function recovery 6 months after injury in TCSCI patients. This retrospective study included complete data of 315 TCSCI patients. The dataset consisting of the first AMS at ≤ 24 h post-injury and follow-up AMS at 6 months post-injury was divided into a training set (80%) and a test set (20%). The nested ensemble algorithm was established in a two-stage manner. Support Vector Classification (SVC), Adaboost, Weak-learner and Dummy were used in the first stage, and Adaboost was selected as second-stage model. The prediction results of the first stage models were uploaded into second-stage model to obtain the final prediction results. The model performance was evaluated using precision, recall, accuracy, F1 score, and confusion matrix. The nested ensemble algorithm was applied to predict motor function recovery of TCSCI, achieving an accuracy of 80.6%, a F1 score of 80.6%, and balancing sensitivity and specificity. The confusion matrix showed few false-negative rate, which has crucial practical implications for prognostic prediction of TCSCI. This novel nested ensemble algorithm, simply based on very early AMS, provides a useful tool for predicting motor function recovery 6 months after TCSCI, which is graded in gradients that progressively improve the accuracy and reliability of the prediction, demonstrating a strong potential of ensemble learning to personalize and optimize the rehabilitation and care of TCSCI patients.

Population-based detection of children ASD/ADHD comorbidity from atypical sensory processing

Comorbidity between neurodevelopmental disorders is common, especially between autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). This study aimed to detect overlapped sensory processing alterations in a sample of children and adolescents diagnosed with both ASD and ADHD. A collection of 42 standard and 8 proposed machine learning classifiers, 22 feature selection methods and 19 unbalanced classification strategies were applied on the 6 standard question groups of the Sensory Profile-2 questionnaire. The relatively low performance achieved by state-of-the-art classifiers led us to propose the feature population sum classifier, a probabilistic method based on class and feature value populations, designed for datasets where features are discrete numeric answers to questions in a questionnaire. The proposed method achieves the best kappa and accuracy, 60% and 82.5%, respectively, reaching 68% and 86.5% combined with backward sequential feature selection, with false positive and negative rates below 15%. Since the SP2 questionnaire can be filled by parents for children from three years, our prediction can alert the clinicians with an early diagnosis in order to apply early interventions.

Enhancing IoT Routing Security and Efficiency: Towards AI-Enabled RPL Protocol

In the rapidly evolving landscape of the Internet of Things (IoT), the security and efficiency of network routing are paramount. The standard Routing Protocol for Low-Power and Lossy Networks (RPL) faces challenges in the form of various cyber-attacks, impacting its reliability and performance. To address this problem, we propose a Novel Algorithm for Network Traffic Analysis and Response (NANTAR), a novel algorithm that optimizes the security and efficiency of RPL routing using AI-based techniques. Through evaluation against traditional RPL, NANTAR has demonstrated remarkable improvements in key performance metrics: a 20% increase in throughput, latency reductions of 20-30%, and more efficient resource utilization. Notably, NANTAR maintains robust network performance with scaling, evidenced by 15-20% lower false positive and negative rates. The algorithm’s efficacy is further highlighted by its high detection rates in the face of diverse attacks and its power consumption efficiency, which is crucial for IoT devices. NANTAR’s adaptability and seamless integration across various IoT platforms affirm its potential as a solution for securing IoT ecosystems, with results indicating significant improvements in key operational metrics.

Axillary management and long-term oncologic outcomes in breast cancer patients with clinical N1 disease treated with neoadjuvant chemotherapy

BackgroundLow false negative rates can be achieved with sentinel lymph node biopsy (SLNB) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients with clinical N1 (cN1) disease. We examined changes in axillary management and oncologic outcomes in BC patients with cN1 disease receiving NAC.MethodsBC patients with biopsy proven cN1 disease treated with NAC were selected from our institutional cancer registry (2014–2017). Patients were grouped by axillary management, axillary lymph node dissection (ALND), SLNB followed by ALND, or SLNB alone. Univariable and multivariable survival analysis for recurrence-free survival (RFS) and overall survival (OS) were performed.Results81 patients met inclusion criteria: 31 (38%) underwent ALND, 25 (31%) SLNB + ALND, and 25 (31%) SLNB alone. A SLN was identified in 45/50 (90%) patients who had SLNB. ALND was performed in 25/50 (50%) patients who had SLNB: 18 for a + SLNB, 5 failed SLNB, and 2 insufficient SLNs. 25 patients had SLNB alone, 17 were SLN- and 8 SLN+. In the SLNB alone group, 23/25 (92%) patients received adjuvant radiation (RT). 20 (25%) patients developed BC recurrence: 14 distant (70%), 3 local (15%), 2 regional + distant (10%), and 1 contralateral (5%). In the SLNB alone group, there was 1 axillary recurrence in a patient with a negative SLNB who did not receive RT. Univariable survival analysis showed significant differences in RFS and OS between axillary management groups, ALND/SLNB + ALND vs. SLNB alone (RFS: p = 0.006, OS: p = 0.021). On multivariable survival analysis, worse RFS and OS were observed in patients with TNBC (RFS: HR 3.77, 95% CI 1.70–11.90, p = 0.023; OS: HR 8.10, 95% CI 1.84–35.60, p = 0.006).ConclusionsSLNB alone and RT after NAC in BC patients with cN1 disease who have negative SLNs at surgery provides long-term regional disease control. This analysis provides support for the practice of axillary downstaging with NAC and SLNB alone.

Is England’s Office for Students likely to falsely identify courses as below threshold on the B3 progression metric?

ABSTRACT Rising inflation and slow growth globally have led to governments focussing on value for money, and in the sphere of higher education policy that is often translated into graduate outcomes. In England, the Office for Students (OfS) has created the progression metric which for full-time, first degree and UK domiciled students requires courses to have 60% of graduates with positive outcomes 15 months after graduation. This paper utilises simulations to evaluate the likelihood that the OfS’s approach will fail to identify courses below the threshold (false positives) and incorrectly identify courses at or above the threshold (false negatives). The simulation adapts the levels of positive outcomes within the population (20-90%), sample size (40–1000), percentage sampled (30-90%) and crucially confidence intervals applied (90%, 95% and 99%) to identify courses below threshold. The three main findings are: the choice of 90% CI minimises the likelihood of false positives compared to 95% or 99% CIs; second, larger samples are essential to reduce likelihood of false positives; and third, preferring the 95% confidence interval instead of 99% for taking regulatory action will result in more courses incorrectly identified as below threshold. Governments that choose a statistical approach to regulate educational standards, like the application of confidence intervals to identify provision below quality standards, should utilise simulations to check the likelihood of false positives and false negatives since failure to do so could lead to both: (a) courses below standard not being identified and (b) regulatory action taken incorrectly against provision at or above standards.

Unveiling the adverse events of Nusinersen in spinal muscular atrophy management based on FAERS database

This study aims to collect and analyze adverse event (AE) reports related to Nusinersen from the FAERS database. The study employed a combination of signal quantification techniques, including the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS), to enhance the accuracy of signal detection and reduce the risk of false positives or negatives. Between the first quarter of 2017 and the third quarter of 2023, the FAERS database collected a total of 11,485,105 drug AE reports, of which 5772 were related to Nusinersen. Through signal mining analysis, 218 preferred term (PT) signals involving 27 system organ classes (SOCs) were identified. The study discovered AEs related to metabolism and nutrition disorders, psychiatric disorders, and cardiac disorders SOCs, which were not mentioned in the product information. Additionally, complications directly related to the intrathecal administration of Nusinersen, such as increased CSF pressure, positive CSF red blood cell count, and AEs related to the method of drug use, such as neuromuscular scoliosis and cerebrospinal fluid reservoir placement, were highlighted. Notably, AEs related to renal function abnormalities, such as abnormal Urine protein/creatinine ratio and protein urine presence, showed higher frequency and signal strength. The findings of this study emphasize the importance of comprehensive safety monitoring in the clinical application of Nusinersen. These results are significant for guiding future clinical practices, improving disease management strategies, and developing safer treatment protocols.