Assessment of reference genes for qRT-PCR normalization to elucidate host response to African swine fever infection.

Viral infection disrupts the normal regulation of the host gene's expression. In order to normalise the expression of dysregulated host genes upon virus infection, analysis of stable reference housekeeping genes using quantitative real-time-PCR (qRT-PCR) is necessary. In the present study, healthy and African swine fever virus (ASFV) infected porcine tissues were assessed for the expression stability of five widely used housekeeping genes (HPRT1, B2M, 18 S rRNA, PGK1 and H3F3A) as reference genes using standard algorithm. Total RNA from each tissue sample (lymph node, spleen, kidney, heart and liver) from healthy and ASFV-infected pigs was extracted and subsequently cDNA was synthesized, and subjected to qRT-PCR. Stability analysis of reference genes expression was performed using the Comparative delta CT, geNorm, BestKeeper and NormFinder algorithm available at RefFinder for the different groups. Direct Cycle threshold (CT) values of samples were used as an input for the web-based tool RefFinder. HPRT1 in spleen, 18 S rRNA in liver and kidney and H3F3A in heart and lymph nodes were found to be stable in the individual healthy tissue group (group A). The majority of the ASFV-infected organs (liver, kidney, heart, lymph node) exhibited H3F3A as stable reference gene with the exception of the ASFV-infected spleen, where HPRT1 was found to be the stable gene (group B). HPRT1 was found to be stable in all combinations of all CT values of both healthy and ASFV-infected porcine tissues (group C). Of five different reference genes investigated for their stability in qPCR analysis, the present study revealed that the 18 S rRNA, H3F3A and HPRT1 genes were optimal reference genes in healthy and ASFV-infected different porcine tissue samples. The study revealed the stable reference genes found in healthy as well as ASF-infected pigs and these reference genes identified through this study will form the baseline data which will be very useful in future investigations on gene expression in ASFV-infected pigs.

Moving towards Normalization of haemostasis and health equity: Evolving treatment goals for haemophilia A.

BACKGROUND: Treatment options for people with haemophilia are evolving at a rapid pace and a range of prophylactic treatment options using various technologies are currently available, each with their own distinct safety and efficacy profile. TREATMENT GOALS: The access to replacement therapy and prophylaxis has driven a dramatic reduction in mortality and resultant increase in life expectancy. Beyond this, the abolition of bleeds and preservation of joint health represent the expected, but rarely attained, goals of haemophilia treatment and care. These outcomes also do not address the complexity of health-related quality of life impacted by haemophilia and its treatment. CONCLUSION: Capitalizing on the major potential of therapeutic innovations, 'Normalization' of haemostasis, as a concept, should include the aspiration of enabling individuals to live as normal a life as possible, free from haemophilia-imposed limitations. To achieve this-being supported by the data reviewed in this manuscript-the concept of haemostatic and life Normalization needs to be explored and debated within the wider multidisciplinary teams and haemophilia community.

Understanding the challenges of identifying, supporting, and signposting patients with alcohol use disorder in secondary care hospitals, post COVID-19: a qualitative analysis from the North East and North Cumbria, England.

BACKGROUND: Alcohol-related mortality and morbidity increased during the COVID-19 pandemic in England, with people from lower-socioeconomic groups disproportionately affected. The North East and North Cumbria (NENC) region has high levels of deprivation and the highest rates of alcohol-related harm in England. Consequently, there is an urgent need for the implementation of evidence-based preventative approaches such as identifying people at risk of alcohol harm and providing them with appropriate support. Non-alcohol specialist secondary care clinicians could play a key role in delivering these interventions, but current implementation remains limited. In this study we aimed to explore current practices and challenges around identifying, supporting, and signposting patients with Alcohol Use Disorder (AUD) in secondary care hospitals in the NENC through the accounts of staff in the post COVID-19 context. METHODS: Semi-structured qualitative interviews were conducted with 30 non-alcohol specialist staff (10 doctors, 20 nurses) in eight secondary care hospitals across the NENC between June and October 2021. Data were analysed inductively and deductively to identify key codes and themes, with Normalisation Process Theory (NPT) then used to structure the findings. RESULTS: Findings were grouped using the NPT domains 'implementation contexts' and 'implementation mechanisms'. The following implementation contexts were identified as key factors limiting the implementation of alcohol prevention work: poverty which has been exacerbated by COVID-19 and the prioritisation of acute presentations (negotiating capacity); structural stigma (strategic intentions); and relational stigma (reframing organisational logics). Implementation mechanisms identified as barriers were: workforce knowledge and skills (cognitive participation); the perception that other departments and roles were better placed to deliver this preventative work than their own (collective action); and the perceived futility and negative feedback cycle (reflexive monitoring). CONCLUSIONS: COVID-19, has generated additional challenges to identifying, supporting, and signposting patients with AUD in secondary care hospitals in the NENC. Our interpretation suggests that implementation contexts, in particular structural stigma and growing economic disparity, are the greatest barriers to implementation of evidence-based care in this area. Thus, while some implementation mechanisms can be addressed at a local policy and practice level via improved training and support, system-wide action is needed to enable sustained delivery of preventative alcohol work in these settings.

Quantifying the pollution changes and meteorological dependence of airborne trace elements coupling source apportionment and machine learning.

Airborne trace elements (TEs) present in atmospheric fine particulate matter (PM2.5) exert notable threats to human health and ecosystems. To explore the impact of meteorological conditions on shaping the pollution characteristics of TEs and the associated health risks, we quantified the variations in pollution characteristics and health risks of TEs due to meteorological impacts using weather normalization and health risk assessment models, and analyzed the source-specific contributions and potential sources of primary TEs affecting health risks using source apportionment approaches at four sites in Shandong Province from September to December 2021. Our results indicated that TEs experience dual effects from meteorological conditions, with a tendency towards higher TE concentrations and related health risks during polluted period, while the opposite occurred during clean period. The total non-carcinogenic and carcinogenic risks of TEs during polluted period increased approximately by factors of 0.53-1.74 and 0.44-1.92, respectively. Selenium (Se), manganese (Mn), and lead (Pb) were found to be the most meteorologically influenced TEs, while chromium (Cr) and manganese (Mn) were identified as the dominant TEs posing health risks. Enhanced emissions of multiple sources for Cr and Mn were found during polluted period. Depending on specific wind speeds, industrialized and urbanized centers, as well as nearby road dusts, could be key sources for TEs. This study suggested that attentions should be paid to not only the TEs from primary emissions but also the meteorology impact on TEs especially during pollution episodes to reduce health risks in the future.

Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review.

Introduction: Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer's disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation. Methods: This review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation. Results: A total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for white matter segmentation tasks in MICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods. Conclusion: Although various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research.

The potential of vascular normalization for sensitization to radiotherapy.

Radiotherapy causes apoptosis mainly through direct or indirect damage to DNA via ionizing radiation, leading to DNA strand breaks. However, the efficacy of radiotherapy is attenuated in malignant tumor microenvironment (TME), such as hypoxia. Tumor vasculature, due to the imbalance of various angiogenic and anti-angiogenic factors, leads to irregular morphology of tumor neovasculature, disordered arrangement of endothelial cells, and too little peripheral coverage. This ultimately leads to a TME characterized by hypoxia, low pH and high interstitial pressure. This deleterious TME further exacerbates the adverse effects of tumor neovascularization and weakens the efficacy of conventional radiotherapy. Whereas normalization of blood vessels improves TME and thus the efficacy of radiotherapy. In addition to describing the research progress of radiotherapy sensitization and vascular normalization, this review focuses on the strategy and application prospect of modulating vascular normalization to improve the efficacy of radiotherapy sensitization.

Beyond COVID-19: Wastewater-based epidemiology for multipathogen surveillance and normalization strategies.

Wastewater-based epidemiology (WBE) is a critical tool for monitoring community health. Although much attention has focused on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a causative agent of coronavirus disease 2019 (COVID-19), other pathogens also pose significant health risks. This study quantified the presence of SARS-CoV-2, influenza A virus (Inf-A), and noroviruses of genogroups I (NoV-GI) and II (NoV-GII) in wastewater samples collected weekly (n = 170) from July 2023 to February 2024 from five wastewater treatment plants (WWTPs) in Yamanashi Prefecture, Japan, by quantitative PCR. Inf-A RNA exhibited localized prevalence with positive ratios of 59 %-82 % in different WWTPs, suggesting regional outbreaks within specific areas. NoV-GI (94 %, 160/170) and NoV-GII (100 %, 170/170) RNA were highly prevalent, with NoV-GII (6.1 ± 0.8 log10 copies/L) consistently exceeding NoV-GI (5.4 ± 0.7 log10 copies/L) RNA concentrations. SARS-CoV-2 RNA was detected in 100 % of the samples, with mean concentrations of 5.3 ± 0.5 log10 copies/L in WWTP E and 5.8 ± 0.4 log10 copies/L each in other WWTPs. Seasonal variability was evident, with higher concentrations of all pathogenic viruses during winter. Non-normalized and normalized virus concentrations by fecal indicator bacteria (Escherichia coli and total coliforms), an indicator virus (pepper mild mottle virus (PMMoV)), and turbidity revealed significant positive associations with the reported disease cases. Inf-A and NoV-GI + GII RNA concentrations showed strong correlations with influenza and acute gastroenteritis cases, particularly when normalized to E. coli (Spearman's ρ = 0.70-0.81) and total coliforms (ρ = 0.70-0.81), respectively. For SARS-CoV-2, non-normalized concentrations showed a correlation of 0.61, decreasing to 0.31 when normalized to PMMoV, suggesting that PMMoV is unsuitable. Turbidity normalization also yielded suboptimal results. This study underscored the importance of selecting suitable normalization parameters tailored to specific pathogens for accurate disease trend monitoring using WBE, demonstrating its utility beyond COVID-19 surveillance.

Evaluation of normalization methods for predicting quantitative phenotypes in metagenomic data analysis.

Genotype-to-phenotype mapping is an essential problem in the current genomic era. While qualitative case-control predictions have received significant attention, less emphasis has been placed on predicting quantitative phenotypes. This emerging field holds great promise in revealing intricate connections between microbial communities and host health. However, the presence of heterogeneity in microbiome datasets poses a substantial challenge to the accuracy of predictions and undermines the reproducibility of models. To tackle this challenge, we investigated 22 normalization methods that aimed at removing heterogeneity across multiple datasets, conducted a comprehensive review of them, and evaluated their effectiveness in predicting quantitative phenotypes in three simulation scenarios and 31 real datasets. The results indicate that none of these methods demonstrate significant superiority in predicting quantitative phenotypes or attain a noteworthy reduction in Root Mean Squared Error (RMSE) of the predictions. Given the frequent occurrence of batch effects and the satisfactory performance of batch correction methods in predicting datasets affected by these effects, we strongly recommend utilizing batch correction methods as the initial step in predicting quantitative phenotypes. In summary, the performance of normalization methods in predicting metagenomic data remains a dynamic and ongoing research area. Our study contributes to this field by undertaking a comprehensive evaluation of diverse methods and offering valuable insights into their effectiveness in predicting quantitative phenotypes.

Evaluating normalized registration and preprocessing methodologies for the analysis of brain MRI in pediatric patients with shunt-treated hydrocephalus.

Introduction: Registration to a standardized template (i.e. "normalization") is a critical step when performing neuroimaging studies. We present a comparative study involving the evaluation of general-purpose registration algorithms for pediatric patients with shunt treated hydrocephalus. Our sample dataset presents a number of intersecting challenges for registration, representing the potentially large deformations to both brain structures and overall brain shape, artifacts from shunts, and morphological differences corresponding to age. The current study assesses the normalization accuracy of shunt-treated hydrocephalus patients using freely available neuroimaging registration tools. Methods: Anatomical neuroimages from eight pediatric patients with shunt-treated hydrocephalus were normalized. Four non-linear registration algorithms were assessed in addition to the preprocessing steps of skull-stripping and bias-correction. Registration accuracy was assessed using the Dice Coefficient (DC) and Hausdorff Distance (HD) in subcortical and cortical regions. Results: A total of 592 registrations were performed. On average, normalizations performed using the brain extracted and bias-corrected images had a higher DC and lower HD compared to full head/ non-biased corrected images. The most accurate registration was achieved using SyN by ANTs with skull-stripped and bias corrected images. Without preprocessing, the DARTEL Toolbox was able to produce normalized images with comparable accuracy. The use of a pediatric template as an intermediate registration did not improve normalization. Discussion: Using structural neuroimages from patients with shunt-treated pediatric hydrocephalus, it was demonstrated that there are tools which perform well after specified pre-processing steps were taken. Overall, these results provide insight to the performance of registration programs that can be used for normalization of brains with complex pathologies.

RA-Net: reverse attention for generalizing residual learning.

Since residual learning was proposed, identity mapping has been widely utilized in various neural networks. The method enables information transfer without any attenuation, which plays a significant role in training deeper networks. However, interference with unhindered transmission also affects the network's performance. Accordingly, we propose a generalized residual learning architecture called reverse attention (RA), which applies high-level semantic features to supervise low-level information in the identity mapping branch. It means that higher semantic features selectively transmit low-level information to deeper layers. In addition, we propose a Modified Global Response Normalization(M-GRN) to implement reverse attention. RA-Net is derived by embedding M-GRN in the residual learning framework. The experiments show that the RA-Net brings significant improvements over residual networks on typical computer vision tasks. For classification on ImageNet-1K, compared with resnet101, RA-Net improves the Top-1 accuracy by 1.7% with comparable parameters and computational cost. For COCO detection, on Faster R-CNN, reverse attention improves box AP by 1.9%. Meanwhile, reverse attention improves UpperNet's mIoU by 0.7% on ADE20K segmentation.

Environmentally responsive hydrogel promotes vascular normalization to enhance STING anti-tumor immunity.

The immunosuppressive microenvironment of malignant tumors severely hampers the effectiveness of anti-tumor therapy. Moreover, abnormal tumor vasculature interacts with immune cells, forming a vicious cycle that further interferes with anti-tumor immunity and promotes tumor progression. Our pre-basic found excellent anti-tumor effects of c-di-AMP and RRx-001, respectively, and we further explored whether they could be combined synergistically for anti-tumor immunotherapy. We chose to load these two drugs on PVA-TSPBA hydrogel scaffolds that expressly release drugs within the tumor microenvironment by in situ injection. Studies have shown that c-di-AMP activates the STING pathway, enhances immune cell infiltration, and reverses tumor immunosuppression. Meanwhile, RRx-001 releases nitric oxide, which increases oxidative stress injury in tumor cells and promotes apoptosis. Moreover, the combination of the two presented more powerful pro-vascular normalization and reversed tumor immunosuppression than the drug alone. This study demonstrates a new design option for anti-tumor combination therapy and the potential of tumor environmentally responsive hydrogel scaffolds in combination with anti-tumor immunotherapy.

BrainCDNet: a concatenated deep neural network for the detection of brain tumors from MRI images.

Introduction: Brain cancer is a frequently occurring disease around the globe and mostly developed due to the presence of tumors in/around the brain. Generally, the prevalence and incidence of brain cancer are much lower than that of other cancer types (breast, skin, lung, etc.). However, brain cancers are associated with high mortality rates, especially in adults, due to the false identification of tumor types, and delay in the diagnosis. Therefore, the minimization of false detection of brain tumor types and early diagnosis plays a crucial role in the improvement of patient survival rate. To achieve this, many researchers have recently developed deep learning (DL)-based approaches since they showed a remarkable performance, particularly in the classification task. Methods: This article proposes a novel DL architecture named BrainCDNet. This model was made by concatenating the pooling layers and dealing with the overfitting issues by initializing the weights into layers using 'He Normal' initialization along with the batch norm and global average pooling (GAP). Initially, we sharpen the input images using a Nimble filter, which results in maintaining the edges and fine details. After that, we employed the suggested BrainCDNet for the extraction of relevant features and classification. In this work, two different forms of magnetic resonance imaging (MRI) databases such as binary (healthy vs. pathological) and multiclass (glioma vs. meningioma vs. pituitary) are utilized to perform all these experiments. Results and discussion: Empirical evidence suggests that the presented model attained a significant accuracy on both datasets compared to the state-of-the-art approaches, with 99.45% (binary) and 96.78% (multiclass), respectively. Hence, the proposed model can be used as a decision-supportive tool for radiologists during the diagnosis of brain cancer patients.

Nano-sensitizer with self-amplified drug release and hypoxia normalization properties potentiates efficient chemoradiotherapy of pancreatic cancer.

The hypoxic nature of pancreatic cancer, one of the most lethal malignancies worldwide, significantly impedes the effectiveness of chemoradiotherapy. Although the development of oxygen carriers and hypoxic sensitizers has shown promise in overcoming tumor hypoxia. The heterogeneity of hypoxia-primarily caused by limited oxygen penetration-has posed challenges. In this study, we designed a hypoxia-responsive nano-sensitizer by co-loading tirapazamine (TPZ), KP372-1, and MK-2206 in a metronidazole-modified polymeric vesicle. This nano-sensitizer relies on efficient endogenous NAD(P)H quinone oxidoreductase 1-mediated redox cycling induced by KP372-1, continuously consuming periphery oxygen and achieving evenly distributed hypoxia. Consequently, the normalized tumor microenvironment facilitates the self-amplified release and activation of TPZ without requiring deep penetration. The activated TPZ and metronidazole further sensitize radiotherapy, significantly reducing the radiation dose needed for extensive cell damage. Additionally, the coloaded MK-2206 complements inhibition of therapeutic resistance caused by Akt activation, synergistically enhancing the hypoxic chemoradiotherapy. This successful hypoxia normalization strategy not only overcomes hypoxia resistance in pancreatic cancer but also provides a potential universal approach to sensitize hypoxic tumor chemoradiotherapy by reshaping the hypoxic distribution.

Defining the transition from new to normal: a qualitative investigation of the clinical change process.

Background: Understanding how and when a new evidence-based clinical intervention becomes standard practice is crucial to ensure that healthcare is delivered in alignment with the most up-to-date knowledge. However, rigorous methods are needed to determine when a new clinical practice becomes normalized to the standard of care. To address this gap, this study qualitatively explores how, when, and why a clinical practice change becomes normalized within healthcare organizations. Methods: We used purposive sampling to recruit clinical leaders who worked in implementation science across diverse health contexts. Enrolled participants completed semi-structured interviews. Qualitative data analysis was guided by a modified version of the Normalization Process Theory (NPT) framework to identify salient themes. Identified normalization strategies were mapped to the Expert Recommendations for Implementation Change (ERIC) project. Results: A total of 17 individuals were interviewed. Participants described four key signals for identifying when a novel clinical practice becomes the new normal: 1) integration into existing workflows; 2) scaling across the entire organizational unit; 3) staff buy-in and ownership; and 4) sustainment without ongoing monitoring. Participants identified salient strategies to normalize new clinical interventions: 1) taking a patient approach; 2) gaining staff buy-in and ownership; and 3) conducting ongoing measurement of progress towards normalization. Conclusions: The results offer valuable insight into the indicators that signify when a novel clinical practice becomes normalized, and the strategies employed to facilitate this transition. These findings can inform future research to develop instruments that implementation leaders can use to systematically measure the clinical change process.

Automated segmentation of ventricular volumes and subarachnoid hemorrhage from computed tomography images: Evaluation of a rule-based pipeline approach.

Changes in ventricular size, related to brain edema and hydrocephalus, as well as the extent of hemorrhage are associated with adverse outcomes in patients with subarachnoid hemorrhage (SAH). Frequently, these are measured manually using consecutive non-contrast computed tomography scans. Here, we developed a rule-based approach which incorporates both intensity and spatial normalization and utilizes user-defined thresholds and anatomical templates to segment both lateral ventricle (LV) and SAH blood volumes automatically from CT images. The algorithmic segmentations were evaluated against two expert neuroradiologists on representative slices from 20 admission scans from aneurysmal SAH patients. Previous methods have been developed to automate this time-consuming task, but they lack user feedback and are hard to implement due to large-scale data and complex design processes. Our results using automatic ventricular segmentation aligned well with expert reviewers with a median Dice coefficient of 0.81, AUC of 0.91, sensitivity of 81%, and precision of 84%. Automatic segmentation of SAH blood was most reliable near the base of the brain with a median Dice coefficient of 0.51, an AUC of 0.75, precision of 68%, and sensitivity of 50%. Ultimately, we developed a rule-based method that is easily adaptable through user feedback, generates spatially normalized segmentations that are comparable regardless of brain morphology or acquisition conditions, and automatically segments LV with good overall reliability and basal SAH blood with good precision. Our approach could benefit longitudinal studies in patients with SAH by streamlining assessment of edema and hydrocephalus progression, as well as blood resorption.

Endoscopic Normalization and Transition of J-Pouch Phenotypes Over Time in Patients With Inflammatory Bowel Disease.

BACKGROUND: Patients with inflammatory bowel disease (IBD) who undergo proctocolectomy with ileal pouch-anal anastomosis may develop pouchitis. We previously proposed a novel endoscopic classification of pouchitis describing 7 phenotypes with differing outcomes. This study assessed phenotype transitions over time. METHODS: We classified pouch findings into 7 main phenotypes: (1) normal, (2) afferent limb (AL) involvement, (3) inlet (IL) involvement, (4) diffuse, (5) focal inflammation of the pouch body, (6) cuffitis, and (7) pouch-related fistulas noted more than 6 months after ileostomy takedown. Among 2 endoscopic phenotypes, the phenotype that was first identified was defined as the primary phenotype, and the phenotype observed later was defined as the subsequent phenotype. RESULTS: We retrospectively reviewed 1359 pouchoscopies from 426 patients (90% preoperative diagnosis of ulcerative colitis). The frequency of primary phenotype was 31% for AL involvement, 42% for IL involvement, 28% for diffuse inflammation, 72% for focal inflammation, 45% for cuffitis, 18% for pouch-related fistulas, and 28% for normal pouch. The most common subsequent phenotype was focal inflammation (64.8%), followed by IL involvement (38.6%), cuffitis (37.8%), AL involvement (25.6%), diffuse inflammation (23.8%), normal pouch (22.8%), and pouch-related fistulas (11.9%). Subsequent diffuse inflammation, pouch-related fistulas, and AL or IL stenoses significantly increased the pouch excision risk. Patients who achieved subsequent normal pouch were less likely to have pouch excision than those who did not (8.1% vs 15.7%; P = .15). CONCLUSIONS: Pouch phenotype and the risk of pouch loss can change over time. In patients with pouch inflammation, subsequent pouch normalization is feasible and associated with favorable outcome.

Endoscopic pouch phenotypes can change over time and subsequent development of diffuse inflammation, pouch-related fistulas, and afferent limb/inlet stenoses significantly worsen pouch outcomes. In patients with pouch inflammation, subsequent pouch normalization is feasible and associated with favorable outcomes.

Differential centiloid scale normalization techniques: comparison between hybrid PET/MRI and independently acquired MRI.

OBJECTIVE: Centiloid (CL) scales play an important role in semiquantitative analyses of amyloid-ß (Aß) PET. CLs are derived from the standardized uptake value ratio (SUVR), which needs Aß positron emission tomography (PET) normalization processing. There are two methods to collect the T1-weighted imaging (T1WI) for normalization: (i) anatomical standardization using simultaneously acquired T1WI (PET/MRI), usually adapted to PET images from PET/MRI scanners, and (ii) T1WI from a separate examination (PET + MRI), usually adapted to PET images from PET/CT scanners. This study aimed to elucidate the correlations and differences in CLs between when using the above two T1WI collection methods. METHODS: Among patients who underwent Aß PET/MRI (using 11C-Pittuberg compound B (11C-PiB) or 18F-flutemetamol (18F-FMM)) at our institution from 2015 to 2023, we selected 49 patients who also underwent other additional MRI examinations, including T1WI for anatomic standardization within 3 years. Thirty-one of them underwent 11C-PiB PET/MRI, and 18 participants underwent 18F-FMM PET/MRI. Twenty-five of them, additional MRI acquisition parameters were identical to simultaneous MRI during PET, and 24 participants were different. After normalization using PET/MRI or PET + MRI method each, SUVR was measured using the Global Alzheimer's Association Initiative Network cerebral cortical and striatum Volume of Interest templates (VOI) and whole cerebellum VOI. Subsequently, CLs were calculated using the previously established equations for each Aß PET tracer. RESULTS: Between PET/MRI and PET + MRI methods, CLs correlated linearly in 11C-PiB PET (y = 1.00x - 0.11, R2 = 0.999), 18F-FMM PET (y = 0.97x - 0.12, 0.997), identical additional MRI acquisition (y = 1.00x + 0.33, 0.999), different acquisition (y = 0.98x - 0.43, 0.997), and entire study group (y = 1.00x - 0.24, 0.999). Wilcoxon signed-rank test revealed no significant differences: 11C-PiB (p = 0.49), 18F-FMM (0.08), and whole PET (0.46). However, significant differences were identified in identical acquisition (p = 0.04) and different acquisition (p = 0.02). Bland-Altman analysis documented only a small bias between PET/MRI and PET + MRI in 11C-PiB PET, 18F-FMM PET, identical additional MRI acquisition, different acquisition, and whole PET (- 0.05, 0.67, - 0.30, 0.78, and 0.21, respectively). CONCLUSIONS: Anatomical standardizations using PET/MRI and using PET + MRI can lead to almost equivalent CL. The CL values obtained using PET/MRI or PET + MRI normalization methods are consistent and comparable in clinical studies.

EfficientNet-deep quantum neural network-based economic denial of sustainability attack detection to enhance network security in cloud.

Cloud computing (CC) is a future revolution in the Information technology (IT) and Communication field. Security and internet connectivity are the common major factors to slow down the proliferation of CC. Recently, a new kind of denial of service (DDoS) attacks, known as Economic Denial of Sustainability (EDoS) attack, has been emerging. Though EDoS attacks are smaller at a moment, it can be expected to develop in nearer prospective in tandem with progression in the cloud usage. Here, EfficientNet-B3-Attn-2 fused Deep Quantum Neural Network (EfficientNet-DQNN) is presented for EDoS detection. Initially, cloud is simulated and thereafter, considered input log file is fed to perform data pre-processing. Z-Score Normalization ;(ZSN) is employed to carry out pre-processing of data. Afterwards, feature fusion (FF) is accomplished based on Deep Neural Network (DNN) with Kulczynski similarity. Then, data augmentation (DA) is executed by oversampling based upon Synthetic Minority Over-sampling Technique (SMOTE). At last, attack detection is conducted utilizing EfficientNet-DQNN. Furthermore, EfficientNet-DQNN is formed by incorporation of EfficientNet-B3-Attn-2 with DQNN. In addition, EfficientNet-DQNN attained 89.8% of F1-score, 90.4% of accuracy, 91.1% of precision and 91.2% of recall using BOT-IOT dataset at K-Fold is 9.

Fine-tuning large language models for rare disease concept normalization.

OBJECTIVE: We aim to develop a novel method for rare disease concept normalization by fine-tuning Llama 2, an open-source large language model (LLM), using a domain-specific corpus sourced from the Human Phenotype Ontology (HPO). METHODS: We developed an in-house template-based script to generate two corpora for fine-tuning. The first (NAME) contains standardized HPO names, sourced from the HPO vocabularies, along with their corresponding identifiers. The second (NAME+SYN) includes HPO names and half of the concept's synonyms as well as identifiers. Subsequently, we fine-tuned Llama 2 (Llama2-7B) for each sentence set and conducted an evaluation using a range of sentence prompts and various phenotype terms. RESULTS: When the phenotype terms for normalization were included in the fine-tuning corpora, both models demonstrated nearly perfect performance, averaging over 99% accuracy. In comparison, ChatGPT-3.5 has only ∼20% accuracy in identifying HPO IDs for phenotype terms. When single-character typos were introduced in the phenotype terms, the accuracy of NAME and NAME+SYN is 10.2% and 36.1%, respectively, but increases to 61.8% (NAME+SYN) with additional typo-specific fine-tuning. For terms sourced from HPO vocabularies as unseen synonyms, the NAME model achieved 11.2% accuracy, while the NAME+SYN model achieved 92.7% accuracy. CONCLUSION: Our fine-tuned models demonstrate ability to normalize phenotype terms unseen in the fine-tuning corpus, including misspellings, synonyms, terms from other ontologies, and laymen's terms. Our approach provides a solution for the use of LLMs to identify named medical entities from clinical narratives, while successfully normalizing them to standard concepts in a controlled vocabulary.

Gene count normalization in single-cell imaging-based spatially resolved transcriptomics.

BACKGROUND: Recent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations in fixed tissues. Normalization of gene expression data is often needed to account for technical factors that may confound underlying biological signals. RESULTS: Here, we investigate the potential impact of different gene count normalization methods with different targeted gene panels in the analysis and interpretation of im-SRT data. Using different simulated gene panels that overrepresent genes expressed in specific tissue regions or cell types, we demonstrate how normalization methods based on detected gene counts per cell differentially impact normalized gene expression magnitudes in a region- or cell type-specific manner. We show that these normalization-induced effects may reduce the reliability of downstream analyses including differential gene expression, gene fold change, and spatially variable gene analysis, introducing false positive and false negative results when compared to results obtained from gene panels that are more representative of the gene expression of the tissue's component cell types. These effects are not observed with normalization approaches that do not use detected gene counts for gene expression magnitude adjustment, such as with cell volume or cell area normalization. CONCLUSIONS: We recommend using non-gene count-based normalization approaches when feasible and evaluating gene panel representativeness before using gene count-based normalization methods if necessary. Overall, we caution that the choice of normalization method and gene panel may impact the biological interpretation of the im-SRT data.