A clinically relevant computed tomography (CT) radiomics strategy for intracranial rodent brain tumour monitoring.

Here, we establish a CT-radiomics based method for application in invasive, orthotopic rodent brain tumour models. Twenty four NOD/SCID mice were implanted with U87R-Luc2 GBM cells and longitudinally imaged via contrast enhanced (CE-CT) imaging. Pyradiomics was employed to extract CT-radiomic features from the tumour-implanted hemisphere and non-tumour-implanted hemisphere of acquired CT-scans. Inter-correlated features were removed (Spearman correlation > 0.85) and remaining features underwent predictive analysis (recursive feature elimination or Boruta algorithm). An area under the curve of the receiver operating characteristic curve was implemented to evaluate radiomic features for their capacity to predict defined outcomes. Firstly, we identified a subset of radiomic features which distinguish the tumour-implanted hemisphere and non- tumour-implanted hemisphere (i.e, tumour presence from normal tissue). Secondly, we successfully translate preclinical CT-radiomic pipelines to GBM patient CT scans (n = 10), identifying similar trends in tumour-specific feature intensities (E.g. 'glszm Zone Entropy'), thereby suggesting a mouse-to-human species conservation (a conservation of radiomic features across species). Thirdly, comparison of features across timepoints identify features which support preclinical tumour detection earlier than is possible by visual assessment of CT scans. This work establishes robust, preclinical CT-radiomic pipelines and describes the application of CE-CT for in-depth orthotopic brain tumour monitoring. Overall we provide evidence for the role of pre-clinical 'discovery' radiomics in the neuro-oncology space.

Machine learning-based identification of contrast-enhancement phase of computed tomography scans.

Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew's correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement.

One Health in Somalia: Present status, opportunities, and challenges.

One Health (OH) is an integrated approach aiming at improving the health of people, animals, and ecosystems. It recognizes the interconnectedness of human health with the health of animals, plants, and the environment. Since Somali people's livelihoods are mainly based on livestock, agriculture, marine resources, and their shared environment, OH-oriented initiatives could significantly impact the country toward reducing complex problems affecting the health of humans, animals, and the environment. The term "One Health" was first introduced into the global scientific community in September 2004 and in 2013 in Somalia. After ten years, there is still a long road ahead for implementing the OH approach in the country. Herein, we present the status, opportunities, and challenges of OH in Somalia and recommend ways to promote and institutionalize it. The country has been involved in various OH initiatives solely driven by external funding, focusing on research, capacity development, and community interventions, apart from university-led initiatives such as Somali One Health Centre. In Somalia, OH initiatives face numerous challenges, ranging from limited infrastructure and resources to weak governance and institutional capacity. We urge the Somali government to address these challenges and prioritize OH as the main approach to tackling critical health issues. We suggest the Somali government institutionalize and implement OH actions at all administrative levels, including Federal, State, District, and community, through a mechanism to improve multisectoral coordination and collaboration to predict, prevent, detect, control, and respond to communicable and non-communicable diseases at the human-animal-ecosystem interface for improving health outcomes for all.

Formal Analysis of Trust and Reputation for Service Composition in IoT.

The exponential growth in the number of smart devices connected to the Internet of Things (IoT) that are associated with various IoT-based smart applications and services, raises interoperability challenges. Service-oriented architecture for IoT (SOA-IoT) solutions has been introduced to deal with these interoperability challenges by integrating web services into sensor networks via IoT-optimized gateways to fill the gap between devices, networks, and access terminals. The main aim of service composition is to transform user requirements into a composite service execution. Different methods have been used to perform service composition, which has been classified as trust-based and non-trust-based. The existing studies in this field have reported that trust-based approaches outperform non-trust-based ones. Trust-based service composition approaches use the trust and reputation system as a brain to select appropriate service providers (SPs) for the service composition plan. The trust and reputation system computes each candidate SP's trust value and selects the SP with the highest trust value for the service composition plan. The trust system computes the trust value from the self-observation of the service requestor (SR) and other service consumers' (SCs) recommendations. Several experimental solutions have been proposed to deal with trust-based service composition in the IoT; however, a formal method for trust-based service composition in the IoT is lacking. In this study, we used the formal method for representing the components of trust-based service management in the IoT, by using higher-order logic (HOL) and verifying the different behaviors in the trust system and the trust value computation processes. Our findings showed that the presence of malicious nodes performing trust attacks leads to biased trust value computation, which results in inappropriate SP selection during the service composition. The formal analysis has given us a clear insight and complete understanding, which will assist in the development of a robust trust system.

Home Management and Prevention of Vaso-Occlusive Crisis of Sickle Cell Disease Knowledge among Nursing Students.

Sickle cell crisis, or vaso-occlusive crisis, is a painful complication of sickle cell disease that occurs in adolescents and adults, which is considered the most common reason these patients seek medical attention in an emergency room. Despite the high prevalence of sickle cell disease in the Jazan region, Saudi Arabia, there hasn't been a study looking at nursing students' knowledge about sickle cell disease and home management and prevention of vaso-occlusive crises associated with sickle cell disease. Most of those focused on the investigation of the public, parents of children with sickle cell disease, school students, and patients with sickle cell disease. Therefore, this study aims to assess the level of knowledge about home management and prevention of vaso-occlusive crises among Saudi nursing students at the Aldayer University College, Jazan University, Kingdom of Saudi Arabia. A descriptive cross-sectional design was used in this study that involved 167 nursing students. The study revealed that Aldayer nursing students had adequate knowledge about the home management and prevention of sickle cell disease vaso-occlusive crises.

Deep learning based identification of bone scintigraphies containing metastatic bone disease foci.

PURPOSE: Metastatic bone disease (MBD) is the most common form of metastases, most frequently deriving from prostate cancer. MBD is screened with bone scintigraphy (BS), which have high sensitivity but low specificity for the diagnosis of MBD, often requiring further investigations. Deep learning (DL) - a machine learning technique designed to mimic human neuronal interactions- has shown promise in the field of medical imaging analysis for different purposes, including segmentation and classification of lesions. In this study, we aim to develop a DL algorithm that can classify areas of increased uptake on bone scintigraphy scans. METHODS: We collected 2365 BS from three European medical centres. The model was trained and validated on 1203 and 164 BS scans respectively. Furthermore we evaluated its performance on an external testing set composed of 998 BS scans. We further aimed to enhance the explainability of our developed algorithm, using activation maps. We compared the performance of our algorithm to that of 6 nuclear medicine physicians. RESULTS: The developed DL based algorithm is able to detect MBD on BSs, with high specificity and sensitivity (0.80 and 0.82 respectively on the external test set), in a shorter time compared to the nuclear medicine physicians (2.5 min for AI and 30 min for nuclear medicine physicians to classify 134 BSs). Further prospective validation is required before the algorithm can be used in the clinic.

The efficacy and safety of a nurse-led electrical cardioversion service for atrial fibrillation over a 2-year time period.

AIMS: Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide, with a significant impact on morbidity, mortality, and utilization of healthcare resources. Electrical direct-current cardioversion (DCCV) is offered to patients with ongoing symptoms despite medical management. In this study, we aim to evaluate the safety and efficacy of a specialized nurse-led DCCV service. METHODS AND RESULTS: This was a retrospective cohort study analysing the outcome of patients presenting with AF or flutter, who were subsequently referred for a nurse-led DCCV procedure between August 2017 and December 2019. Analysis included a total of 341 patients (mean age = 68.37; standard deviation = 10.96) who presented with either AF (N = 267; 78.30%) or atrial flutter (N = 74; 21.70%). Approximately 30% of patients were females (N = 101) and 70% were males (N = 240). Of the 341 patients who underwent DCCV, 299 were successfully cardioverted (87.68%). Of those patients successfully cardioverted, 167 remained in sinus rhythm after 6 weeks (55.85%); 93 patients reverted back to AF (31.10%). Thirty-eight patients were lost to follow up (12.71%). Of all 341 patients who underwent DCCV, only 24 patients were admitted to hospital during the subsequent 3-month period (7.04%). Importantly, no patients were admitted as a direct complication of the DCCV procedure. CONCLUSION: Overall, data gathered from this study provides positive evidence to support the use of a nurse-led DCCV service. In addition to obtaining very successful cardioversion rates, we found low remission rates, with a very low hospital readmission rate for AF-related issues after successful DCCV.

Impact of Coronavirus Disease 2019 Lockdown on Back Pain Intensity, Prevalence and Associated Risk Factor Among Adults in Malaysia

@#Introduction: Low back pain (LBP) is a common musculoskeletal disorder (MSD) that leads to a variety of negative consequences. The prevalence of LBP was found to be high worldwide. Individuals have been forced to remote studying or working conditions during the coronavirus disease 2019 (COVID-19) pandemic, exposing them to the risk factors of LBP to a greater extent. This is a cross-sectional study conducted among 623 adults and aimed to assess LBP intensity and prevalence among adults in Malaysia before and during the COVID-19 lockdown and to identify the association between demographic, physical and psychological factors with LBP. Methods: A self-administered questionnaire composed of 36 questions regarding demographic characteristics, physical activities and psychological aspects was distributed to the public. Pearson’s Chi-square and Fisher Exact tests were performed using collected data to assess the association between LBP intensity and various risk factors. Results: The prevalence of LBP increased from 64.4% before the lockdown to 83.5% during the lockdown. LBP intensity significantly increased during the lockdown. Before lockdown, factors associated with LBP intensity included gender and stress level. While during the lockdown, age, gender, occupation, time spent on computer use, ergonomics as well as stress level affected the intensity of LBP. Conclusion: The present study showed that the COVID-19 lockdown has contributed to the increase in both the prevalence and intensity of LBP among Malaysians. The identified risk factors include age, gender, occupation, duration of sitting and computer use, adherence to ergonomic recommendations, and stress level.

Hemoplasmas and ticks in cattle from Somalia.

Hemotropic mycoplasmas (hemoplasmas) are small Gram-negative bacteria that parasitize red blood cells and can cause mild to severe anemia in a wide range of vertebrates, including ruminants. Cattle population in Somalia is around 3.9 million heads, with animals more concentrated around the river areas, mainly in the Juba River and Shabelle River Valleys. Information on hemoplasmas in Sub-Saharan Africa are scarce, mainly in Somalia, where no studies have been performed to date. Accordingly, this study aimed to assess the molecular occurrence of hemoplasmas in 131 cattle blood samples from Somalia. Thirty out of 131 (22.90%; 95% CI: 16.54-30.81%) cattle were infested by ticks: Rhipicephalus pulchellus (68.18%), Amblyomma gemma (18.18%), Amblyomma lepidum (9.09%), Hyalomma marginatum (1.51%), Hyalmomma rufipes (1.51%), and Rhipicephalus pravus (1.51%). A total of 74/131 (56.48%; 95% CI: 47.93-64.67%) cattle were positive for hemotropic Mycoplasma spp. by real-time PCR (qPCR) based on the 16S rRNA gene. Hemoplasma-positive samples were later subjected to species-specific PCR assays for Mycoplasma wenyonii and 'Candidatus Mycoplasma haematobovis' based on the 16S rRNA gene. A total of 34/74 (45.94%; 95% CI: 35.07-57.22%) animals were coinfected by both species; 31/74 (41.89%; 95% CI: 31.32-53.26%) and 3/74 (4.05%; 95% CI: 01.39-11.25%) cattle were solely positive to M. wenyonii and 'Ca. M. haematobovis', respectively. Six out of 74 (8.1%; 95% CI: 03.77-16.58%) cattle were negative on species-specific conventional PCR assays but tested positive by a semi-nested PCR assay based on the 16S rRNA gene of hemoplasmas. Sequencing of the detected hemotropic Mycoplasma sp. 16S rRNA gene confirmed that animals were infected by M. wenyonii and 'Ca. M. haematobovis'. To the best of our knowledge, this is the first study on the detection of hemoplasmas in cattle from Somalia.

Impact of COVID-19 status on patients receiving neuraxial analgesia during labor: A national retrospective-controlled study.

BACKGROUND: Pregnancy affects a woman's susceptibility to and severity of certain infectious diseases. Central neuraxial block for analgesia during labor is superior to nonneuraxial methods in efficacy, safety, and maternal satisfaction. Although Coronavirus disease (COVID-19) can be vertically transmitted from mother to fetus, little is known about the effects of COVID-19 on pregnant women or about anesthesia management and the risk of adverse effects related to neuraxial techniques in women with untreated COVID-19 during gestation. AIM: This investigation assesses the effects of neuraxial analgesia during labor of COVID-19-positive parturients on their hemodynamic stability. RESULTS: The study was conducted on 64 patients and involved 32 parturients positive for SARS-CoV-2 by polymerase chain reaction (PCR) and a similar number of control "negative" patients. The affected group had an uneventful course during gestation. Seven were positive for ground-glass opacities on chest X-rays, and none underwent computed tomography (CT) scans. Two neonates were PCR-positive for SARS-CoV-2, and all 32 neonates were released from the hospital. No clinical differences were observed between the neonates in the COVID-19 and control groups. Although parturients in both groups were hemodynamically stable, hemodynamic stability was subnormal in the COVID-19 group regarding blood pressure, oxygen saturation, heart rate, and body temperature. None of the women in either group required a vasopressor or oxygen supplementation during delivery. No other clinical differences were observed between the COVID-19 and control groups. CONCLUSION: This is the first case-controlled study testing the anesthetic implications of neuraxial labor analgesia in pregnant, COVID-19-positive women. Although management of neuraxial labor analgesia did not differ in pregnant women positive and negative for COVID-19, their hemodynamic characteristics differed significantly. Therefore, care is required to prevent adverse outcomes in pregnant women positive for COVID-19.

Automated detection and segmentation of non-small cell lung cancer computed tomography images.

Detection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts. Moreover, we demonstrate that on average, radiologists & radiation oncologists preferred automatic segmentations in 56% of the cases. Additionally, we evaluate the prognostic power of the automatic contours by applying RECIST criteria and measuring the tumor volumes. Segmentations by our method stratified patients into low and high survival groups with higher significance compared to those methods based on manual contours.

Peak troponin T in STEMI: a predictor of all-cause mortality and left ventricular function.

BACKGROUND: The clinical significance of peak troponin levels following ST-elevation myocardial infarction (STEMI) has not been definitively established. The purpose of this study was to examine the relationship between peak high-sensitivity cardiac troponin T (hs-cTnT) and all-cause mortality at 30 days and 1 year, and left ventricular ejection fraction (LVEF) in STEMI. METHODS: A single-centre retrospective observational study was conducted of all patients with STEMI between January 2015 and December 2017. Demographics and clinical data were obtained through electronic patient records. Standard Bayesian statistics were employed for analysis. RESULTS: During the study period, 568 patients presented with STEMI. The mean age was 63.6±12 years and 76.4% were men. Of these, 535 (94.2%) underwent primary percutaneous coronary intervention, 12 (2.1%) underwent urgent coronary artery bypass and 21 (3.7%) were treated medically. Mean peak hs-cTnT levels were significantly higher in those who died within 30 days compared with those who survived (12 238 ng/L vs 4657 ng/L, respectively; p=0.004). Peak hs-cTnT levels were also significantly higher in those who died within 1 year compared with those who survived (10 319 ng/L vs 4622 ng/L, respectively; p=0.003). The left anterior descending artery was associated with the highest hs-cTnT and was the most common culprit in those who died at 1 year. An inverse relationship was demonstrated between peak hs-cTnT and LVEF (Pearson's R=0.379; p<0.00001). CONCLUSIONS: In STEMI, those who died at 30 days and 1 year had significantly higher peak troponin levels than those who survived. Peak troponin is also inversely proportional to LVEF with higher troponins associated with lower LVEF.

CT Reconstruction Kernels and the Effect of Pre- and Post-Processing on the Reproducibility of Handcrafted Radiomic Features.

Handcrafted radiomics features (HRFs) are quantitative features extracted from medical images to decode biological information to improve clinical decision making. Despite the potential of the field, limitations have been identified. The most important identified limitation, currently, is the sensitivity of HRF to variations in image acquisition and reconstruction parameters. In this study, we investigated the use of Reconstruction Kernel Normalization (RKN) and ComBat harmonization to improve the reproducibility of HRFs across scans acquired with different reconstruction kernels. A set of phantom scans (n = 28) acquired on five different scanner models was analyzed. HRFs were extracted from the original scans, and scans were harmonized using the RKN method. ComBat harmonization was applied on both sets of HRFs. The reproducibility of HRFs was assessed using the concordance correlation coefficient. The difference in the number of reproducible HRFs in each scenario was assessed using McNemar's test. The majority of HRFs were found to be sensitive to variations in the reconstruction kernels, and only six HRFs were found to be robust with respect to variations in reconstruction kernels. The use of RKN resulted in a significant increment in the number of reproducible HRFs in 19 out of the 67 investigated scenarios (28.4%), while the ComBat technique resulted in a significant increment in 36 (53.7%) scenarios. The combination of methods resulted in a significant increment in 53 (79.1%) scenarios compared to the HRFs extracted from original images. Since the benefit of applying the harmonization methods depended on the data being harmonized, reproducibility analysis is recommended before performing radiomics analysis. For future radiomics studies incorporating images acquired with similar image acquisition and reconstruction parameters, except for the reconstruction kernels, we recommend the systematic use of the pre- and post-processing approaches (respectively, RKN and ComBat).

MaasPenn Radiomics Reproducibility Score: A Novel Quantitative Measure for Evaluating the Reproducibility of CT-Based Handcrafted Radiomic Features.

The reproducibility of handcrafted radiomic features (HRFs) has been reported to be affected by variations in imaging parameters, which significantly affect the generalizability of developed signatures and translation to clinical practice. However, the collective effect of the variations in imaging parameters on the reproducibility of HRFs remains unclear, with no objective measure to assess it in the absence of reproducibility analysis. We assessed these effects of variations in a large number of scenarios and developed the first quantitative score to assess the reproducibility of CT-based HRFs without the need for phantom or reproducibility studies. We further assessed the potential of image resampling and ComBat harmonization for removing these effects. Our findings suggest a need for radiomics-specific harmonization methods. Our developed score should be considered as a first attempt to introduce comprehensive metrics to quantify the reproducibility of CT-based handcrafted radiomic features. More research is warranted to demonstrate its validity in clinical contexts and to further improve it, possibly by the incorporation of more realistic situations, which better reflect real patients' situations.

The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model.

Radiomics, one of the potential methods for developing clinical biomarker, is one of the exponentially growing research fields. In addition to its potential, several limitations have been identified in this field, and most importantly the effects of variations in imaging parameters on radiomic features (RFs). In this study, we investigate the potential of RFs to predict overall survival in patients with clear cell renal cell carcinoma, as well as the impact of ComBat harmonization on the performance of RF models. We assessed the robustness of the results by performing the analyses a thousand times. Publicly available CT scans of 179 patients were retrospectively collected and analyzed. The scans were acquired using different imaging vendors and parameters in different medical centers. The performance was calculated by averaging the metrics over all runs. On average, the clinical model significantly outperformed the radiomic models. The use of ComBat harmonization, on average, did not significantly improve the performance of radiomic models. Hence, the variability in image acquisition and reconstruction parameters significantly affect the performance of radiomic models. The development of radiomic specific harmonization techniques remain a necessity for the advancement of the field.

Analysis of Risk Factors for Patient Safety Events Occurring in the Emergency Department.

OBJECTIVE: The aim of the study was to describe and analyze the risk factors associated with patient safety events (PSEs), defined as adverse events (AEs), preventable AEs (PAEs), and near-miss events (NMEs), in the emergency department (ED). METHODS: It was a retrospective cohort study using ED patients' data retrieved from January 2010 to December 2016. Quality assurance issues (QAIs) used as triggers included the following: issues during procedural sedation, death within 24 hours of admission, patients' and physicians' complaints, returns to the ED within 72 hours, and transfers to an intensive care unit within 24 hours. RESULTS: Of 383,586 ED visits, 6519 (1.7%) QAIs were reported with a PSEs incidence of 6.1%. Among the 397 PSEs, 258 were AEs including 82 PAEs, and 139 NMEs. During the 7-year period, we observed a fourfold increase in NMEs, and despite a decrease in the rate of AEs with the highest (3.1%) and lowest (0.8%) incidence in 2011 and 2016, respectively, the incidence of PAEs events remained relatively constant. Unadjusted analysis showed that ED waiting time, boarding time, ED length of stay (LOS), ED disposition, as well as diagnostic and QAIs were significantly related to PSEs (P < 0.05). Multivariable analysis showed that the type of QAIs and diagnostic were associated with PSEs (P < 0.001). Type of QAIs was a risk factor for AEs and PAEs occurrence and factors involved in NMEs were type of QAIs (P = 0.02) and ED LOS (P < 0.001). "The odds of a PSE occurring increased by 0.2% for each additional minute increase in the ED waiting time, by 5.2% for each additional boarding hour, and by 4.5% for each ED LOS hour." CONCLUSIONS: This study showed several potential risk factors for PSEs, especially ED LOS, type of QAIs, and diagnostic. Systematic interventions might have more impact on risk of PSE.

Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods.

Radiomics converts medical images into mineable data via a high-throughput extraction of quantitative features used for clinical decision support. However, these radiomic features are susceptible to variation across scanners, acquisition protocols, and reconstruction settings. Various investigations have assessed the reproducibility and validation of radiomic features across these discrepancies. In this narrative review, we combine systematic keyword searches with prior domain knowledge to discuss various harmonization solutions to make the radiomic features more reproducible across various scanners and protocol settings. Different harmonization solutions are discussed and divided into two main categories: image domain and feature domain. The image domain category comprises methods such as the standardization of image acquisition, post-processing of raw sensor-level image data, data augmentation techniques, and style transfer. The feature domain category consists of methods such as the identification of reproducible features and normalization techniques such as statistical normalization, intensity harmonization, ComBat and its derivatives, and normalization using deep learning. We also reflect upon the importance of deep learning solutions for addressing variability across multi-centric radiomic studies especially using generative adversarial networks (GANs), neural style transfer (NST) techniques, or a combination of both. We cover a broader range of methods especially GANs and NST methods in more detail than previous reviews.

Reproducibility of CT-Based Hepatocellular Carcinoma Radiomic Features across Different Contrast Imaging Phases: A Proof of Concept on SORAMIC Trial Data.

Handcrafted radiomic features (HRFs) are quantitative imaging features extracted from regions of interest on medical images which can be correlated with clinical outcomes and biologic characteristics. While HRFs have been used to train predictive and prognostic models, their reproducibility has been reported to be affected by variations in scan acquisition and reconstruction parameters, even within the same imaging vendor. In this work, we evaluated the reproducibility of HRFs across the arterial and portal venous phases of contrast-enhanced computed tomography images depicting hepatocellular carcinomas, as well as the potential of ComBat harmonization to correct for this difference. ComBat harmonization is a method based on Bayesian estimates that was developed for gene expression arrays, and has been investigated as a potential method for harmonizing HRFs. Our results show that the majority of HRFs are not reproducible between the arterial and portal venous imaging phases, yet a number of HRFs could be used interchangeably between those phases. Furthermore, ComBat harmonization increased the number of reproducible HRFs across both phases by 1%. Our results guide the pooling of arterial and venous phases from different patients in an effort to increase cohort size, as well as joint analysis of the phases.

Rift Valley fever and Brucella spp. in ruminants, Somalia.

BACKGROUND: Fourteen-years after the last Rift Valley fever (RVF) virus (RVFV) outbreak, Somalia still suffers from preventable transboundary diseases. The tradition of unheated milk consumption and handling of aborted materials poses a public health risk for zoonotic diseases. Limited data are available on RVF and Brucella spp. in Somali people and their animals. Hence, this study has evaluated the occurrence of RVFV and Brucella spp. antibodies in cattle, goats and sheep sera from Afgoye and Jowhar districts of Somalia. METHODS: Serum samples from 609 ruminants (201 cattle, 203 goats and 205 sheep), were serologically screened for RVF by a commercial cELISA, and Brucella species by modified Rose Bengal Plate Test (mRBPT) and a commercial iELISA. RESULTS: Two out of 609 (0.3 %; 95 %CI: 0.04-1.2 %) ruminants were RVF seropositive, both were female cattle from both districts. Anti-Brucella spp. antibodies were detected in 64/609 (10.5 %; 95 %CI: 8.2-13.2 %) ruminants by mRBPT, which were 39/201 (19.4 %) cattle, 16/203 (7.9 %) goats and 9/205 (4.4 %) sheep. Cattle were 5.2 and 2.8 times more likely to be Brucella-seropositive than sheep (p = 0.000003) and goats (p = 0.001), respectively. When mRBPT-positive samples were tested by iELISA, 29/64 (45.3 %; 95 %CI: 32.8-58.3 %) ruminant sera were positive for Brucella spp. Only 23/39 (58.9 %) cattle sera and 6/16 (37.5 %) goat sera were positive to Brucella spp. by iELISA. CONCLUSIONS: The present study showed the serological evidence of RVF and brucellosis in ruminants from Afgoye and Jowhar districts of Somalia. Considering the negligence of the zoonotic diseases at the human-animal interface in Somali communities, a One Health approach is needed to protect public health.

Reply to Orlhac, F.; Buvat, I. Comment on "Ibrahim et al. The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features' Stability with and without ComBat Harmonization. Cancers 2021, 13, 1848".

We would like to thank Orlhac and Buvat [...].