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The current standard of care for many patients with HER2-positive breast cancer is neoadjuvant chemotherapy in combination with anti-HER2 agents, based on HER2 amplification as detected by in situ hybridization (ISH) or protein immunohistochemistry (IHC). However, hematoxylin & eosin (H&E) tumor stains are more commonly available, and accurate prediction of HER2 status and anti-HER2 treatment response from H&E would reduce costs and increase the speed of treatment selection. Computational algorithms for H&E have been effective in predicting a variety of cancer features and clinical outcomes, including moderate success in predicting HER2 status. In this work, we present a novel convolutional neural network (CNN) approach able to predict HER2 status with increased accuracy over prior methods. We trained a CNN classifier on 188 H&E whole slide images (WSIs) manually annotated for tumor Regions of interest (ROIs) by our pathology team. Our classifier achieved an area under the curve (AUC) of 0.90 in cross-validation of slide-level HER2 status and 0.81 on an independent TCGA test set. Within slides, we observed strong agreement between pathologist annotated ROIs and blinded computational predictions of tumor regions / HER2 status. Moreover, we trained our classifier on pre-treatment samples from 187 HER2+ patients that subsequently received trastuzumab therapy. Our classifier achieved an AUC of 0.80 in a five-fold cross validation. Our work provides an H&E-based algorithm that can predict HER2 status and trastuzumab response in breast cancer at an accuracy that may benefit clinical evaluations.

Polyesters containing 2,4-dihydroxy-6-(2-hydroxypropyl)benzoate and 3-hydroxybutyrate moieties have been isolated from many fungal species. Talaromyces stipitatus was previously reported to produce a similar polyester, talapolyester G. The complete genome sequence and the development of bioinformatics tools have enabled the discovery of the biosynthetic potential of this microorganism. Here, a putative biosynthetic gene cluster (BGC) of the polyesters encoding a highly reducing polyketide synthase (HR-PKS) and nonreducing polyketide synthase (NR-PKS), a cytochrome P450 and a regulator, was identified. Although talapolyester G does not require an oxidative step for its biosynthesis, further investigation into the secondary metabolite production of T. stipitatus resulted in isolating two new metabolites called talarodioxadione and talarooxime, in addition to three known compounds, namely 6-hydroxymellein, 15G256α and transtorine that have never been reported from this organism. Interestingly, the biosynthesis of the cyclic polyester 15G256α requires hydroxylation of an inactive methyl group and thus could be a product of the identified gene cluster. The two compounds, talarooxime and transtorine, are probably the catabolic metabolites of tryptophan through the kynurenine pathway. Tryptophan metabolism exists in almost all organisms and has been of interest to many researchers. The biosynthesis of the new oxime is proposed to involve two subsequent N-hydroxylation of 2-aminoacetophenone.

A kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for human detection, requiring significant analysis time. Consequently, developing a detection system becomes crucial for accurately classifying KUB X-ray images. This article applies a transfer learning (TL) model with a pre-trained VGG16 empowered with explainable artificial intelligence (XAI) to establish a system that takes KUB X-ray images and accurately categorizes them as kidney stones or normal cases. The findings demonstrate that the model achieves a testing accuracy of 97.41% in identifying kidney stones or normal KUB X-rays in the dataset used. VGG16 model delivers highly accurate predictions but lacks fairness and explainability in their decision-making process. This study incorporates the Layer-Wise Relevance Propagation (LRP) technique, an explainable artificial intelligence (XAI) technique, to enhance the transparency and effectiveness of the model to address this concern. The XAI technique, specifically LRP, increases the model's fairness and transparency, facilitating human comprehension of the predictions. Consequently, XAI can play an important role in assisting doctors with the accurate identification of kidney stones, thereby facilitating the execution of effective treatment strategies.

Modified multi-walled carbon nanotubes (f-MWCNTs) and hydroxyapatite nanorods (n-HA) were reinforced into polypropylene (PP) with the support of a melt compounding approach. Varying composition of f-MWCNTs (0.1–0.3 wt.%) and nHA (15–20 wt.%) were reinforced into PP, to obtain biocomposites of different compositions. The morphology, thermal and mechanical characteristics of PP/n-HA/f-MWCNTs were observed. Tensile studies reflected that the addition of f-MWCNTs is advantageous in improving the tensile strength of PP/n-HA nanocomposites but decreases its Young’s modulus significantly. Based on the thermal study, the f-MWCNTs and n-HA were known to be adequate to enhance PP’s thermal and dimensional stability. Furthermore, MTT studies proved that PP/n-HA/f-MWCNTs are biocompatible. Consequently, f-MWCNTs and n-HA reinforced into PP may be a promising nanocomposite in orthopedics industry applications such as the human subchondral bone i.e. patella and cartilage and fabricating certain light-loaded implants.

This investigation presents the development and characterization of an advanced piezoelectric perovskite-based biosensing platform optimized for formalin detection in aqueous media through the implementation of Locally Weighted Linear Regression (LWLR) machine learning algorithms. The sensor architecture operates within the terahertz spectral region and incorporates an advanced nanomaterial composite system comprising black phosphorus, gold nanostructures, graphene, and barium titanate to maximize detection sensitivity and operational performance metrics. The engineered platform integrates a circular graphene metasurfaces configuration with a gold-based H-resonator assembly and concentrically arranged circular ring resonators. Computational simulations demonstrate vigorous sensing capabilities across three discrete frequency bands, achieving remarkable sensitivity parameters of 444 GHzRIU⁻¹, accompanied by a quality factor of 5.970 and detection accuracy of 7.576. The integration of LWLR-based optimization protocols substantially enhances prediction accuracy while reducing computational time by ≥ 85% as well as cutting down the required resources. The proposed sensor architecture presents significant potential for environmental monitoring and clinical applications, offering a highly sensitive and efficient methodology for quantitative formalin detection in aqueous environments.

The relationship between intervertebral disc degeneration and chronic infection by Propionibacterium acnes is controversial with contradictory evidence available in the literature. Previous studies investigating these relationships were under-powered and fraught with methodical differences; moreover, they have not taken into consideration P. acnes' ability to form biofilms or attempted to quantitate the bioburden with regard to determining bacterial counts/genome equivalents as criteria to differentiate true infection from contamination. The aim of this prospective cross-sectional study was to determine the prevalence of P. acnes in patients undergoing lumbar disc microdiscectomy. The sample consisted of 290 adult patients undergoing lumbar microdiscectomy for symptomatic lumbar disc herniation. An intraoperative biopsy and pre-operative clinical data were taken in all cases. One biopsy fragment was homogenized and used for quantitative anaerobic culture and a second was frozen and used for real-time PCR-based quantification of P. acnes genomes. P. acnes was identified in 115 cases (40%), coagulase-negative staphylococci in 31 cases (11%) and alpha-hemolytic streptococci in 8 cases (3%). P. acnes counts ranged from 100 to 9000 CFU/ml with a median of 400 CFU/ml. The prevalence of intervertebral discs with abundant P. acnes (≥ 1x103 CFU/ml) was 11% (39 cases). There was significant correlation between the bacterial counts obtained by culture and the number of P. acnes genomes detected by real-time PCR (r = 0.4363, p<0.0001). In a large series of patients, the prevalence of discs with abundant P. acnes was 11%. We believe, disc tissue homogenization releases P. acnes from the biofilm so that they can then potentially be cultured, reducing the rate of false-negative cultures. Further, quantification study revealing significant bioburden based on both culture and real-time PCR minimize the likelihood that observed findings are due to contamination and supports the hypothesis P. acnes acts as a pathogen in these cases of degenerative disc disease.

Background Today’s healthcare organizations want to implement secure and quality healthcare software as cyber-security is a significant risk factor for healthcare data. Considering security requirements during trustworthy healthcare software development process is an essential part of the quality software development. There are several Security Requirements Engineering (SRE) methodologies, framework, process, standards available today. Unfortunately, there is still a necessity to improve these security requirements engineering approaches. Determining the most suitable security requirements engineering method for trustworthy healthcare software development is a challenging process. This study is aimed to present security experts’ perspective on the relative importance of the criteria for selecting effective SRE method by utilizing the multi-criteria decision making methods. Methods The study was planned and conducted to identify the most appropriate SRE approach for quality and trustworthy software development based on the security expert’s knowledge and experience. The hierarchical model was evaluated by using fuzzy TOPSIS model. Effective SRE selection criteria were compared in pairs. 25 security experts were asked to response the pairwise criteria comparison form. Results The impact of the recognized selection criteria for effective security requirements engineering approaches has been evaluated quantitatively. For each of the 25 participants, comparison matrixes were formed based on the scores of their responses in the form. The consistency ratios (CR) were found to be smaller than 10% (CR = 9.1% < 10%). According to pairwise comparisons result; with a 0.842 closeness coefficient (Ci), STORE methodology is the most effective security requirements engineering approach for trustworthy healthcare software development. Conclusions The findings of this research study demonstrate various factors in the decision-making process for the selection of a reliable method for security requirements engineering. This is a significant study that uses multi-criteria decision-making tools, specifically fuzzy TOPSIS, which used to evaluate different SRE methods for secure and trustworthy healthcare application development.

In this paper, a single-layer reconfigurable reflective metasurface is presented. The proposed metasurface operates at 5.4 GHz and can achieve either absorption or cross-polarization conversion corresponding at two different diode biasing states. The reflective metasurface acts as an absorber for an incident wave when the diodes are forward-biased. Similarly, it changes the polarization state of the reflected wave for a linearly polarized incident wave when the diodes are reverse-biased. The proposed structure maintains the aforementioned performance characteristics for oblique incidence, up to 60° compared to the perpendicular incidence. The proposed metasurface can achieve linear to linear polarization conversion with polarization conversion ratio (PCR) > 95% and absorption, with absorption ratio (AR) > 80% in the same frequency band just by reconfiguring the state of the PIN diodes.

This paper proposes a scalable and autonomous malware detection and defence architecture in software-defined networks (SDNs) that employs federated learning (FL). This architecture combines SDN’s centralized management of potentially significant data streams with FL’s decentralized, privacy-preserving learning capabilities in a distributed manner adaptable to varying time and space constraints. This enables a flexible, adaptive design and prevention approach in large-scale, heterogeneous networks. Using balanced datasets, we observed detection rates of up to 96% for controlled DDoS and Botnet attacks. However, in more realistic simulations that utilized diverse, real-world imbalanced datasets (such as CICIDS 2017 and UNSW-NB15) and complex scenarios like data exfiltration, the performance dropped to an overall accuracy of 59.50%. This reflects the challenges encountered in real-world deployments. We analyzed performance metrics such as detection accuracy, latency (less than 1 s), throughput recovery (from 300 to 500 Mbps), and communication overhead comparatively. Our architecture minimizes privacy risks by ensuring that raw data never leaves the device; only model updates are shared for aggregation at the global level. While it effectively detects high-impact incursions, there is room for improvement in identifying more subtle threats, which can be addressed with enriched datasets and improved feature engineering. This work offers a robust, privacy-preserving framework for deploying scalable and intelligent malware detection in contemporary network infrastructures.

The growth of IoT networks has led to significant security issues, especially in areas of real-time threat detection and response. This research paper presents a hybrid deep learning and semantic reasoning framework that enhances threat intelligence and autonomous response. The proposed research framework integrates Convolutional Neural Networks for spatial anomaly detection and Recurrent Neural Networks for sequential pattern recognition. Concurrently, a semantic contextualization layer utilizes knowledge graphs for context-aware threat detection. The model is highly computational and energy efficient, incorporating path-breaking Edge Computing and Real-Time Stream Processing paradigms, facilitating low-latency identification of highly dynamic advanced attacks like APTs and DDoS. During this research study, extensive statistical validation was performed using the CICIoT 2023 dataset and a custom Internet of Things testbed, demonstrating high accuracy, scalability, and adaptability across diverse IoT environments. The paper also outlines privacy, ethical considerations, and regulatory compliance (GDPR, CCPA) to ensure responsible deployment. This research contributes to next-generation autonomous IoT security solutions, bridging deep learning, semantic reasoning, and real-world security challenges, with future work focusing on real-world deployments and adaptive threat intelligence.