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Abstract Background The use of tools that allow estimation of the probability of progression of chronic kidney disease (CKD) to advanced stages has not yet achieved significant practical importance in clinical setting. This study aimed to develop and validate a machine learning-based model for predicting the need for renal replacement therapy (RRT) and disease progression for patients with stage 3–5 CKD. Methods This was a retrospective, closed cohort, observational study. Patients with CKD affiliated with a private insurer with five-year follow-up data were selected. Demographic, clinical, and laboratory variables were included, and the models were developed based on machine learning methods. The outcomes were CKD progression, a significant decrease in the estimated glomerular filtration rate (eGFR), and the need for RRT. Results Three prediction models were developed—Model 1 (risk at 4.5 years, n = 1446) with a F1 of 0.82, 0.53, and 0.55 for RRT, stage progression, and reduction in the eGFR, respectively,— Model 2 (time- to-event, n = 2143) with a C-index of 0.89, 0.67, and 0.67 for RRT, stage progression, reduction in the eGFR, respectively, and Model 3 (reduced Model 2) with C-index = 0.68, 0.68 and 0.88, for RRT, stage progression, reduction in the eGFR, respectively. Conclusion The time-to-event model performed well in predicting the three outcomes of CKD progression at five years. This model can be useful for predicting the onset and time of occurrence of the outcomes of interest in the population with established CKD.

Human monitoring using radar systems operating in the GHz regime has generated significant interest as a result of the increasing availability of commercial radar systems. These sensors offer all weather performance, the ability to measure range and velocity, and the protection of anonymity. However, visually inferring activities present in radar data is often challenging without prior knowledge. Here, we address this by implementing a radar-to-pose system that converts the raw radar data into human poses, such that human forms can be identified and activities monitored. In comparison to prior works, we place our radar in an elevated position, more in line with the placement of existing real world monitoring systems e.g. cameras, or emerging systems, e.g. quadcopters. We present an ensemble predictor network and apply it to a number of human poses of increasing complexity, reporting accuracies in excess of 90%, and verify the generalizable nature of our approach with unseen validation data. We perform an in depth explainability analysis, exploiting the unique mappings created by our radar placement and network structure to confirm that the network is making rational predictions based on the true location of limbs.

A stable scheme is proposed in this paper in order to obtain approximate solutions of second-moment turbulent models for incompressible flows with or without thermal transport equation. The analysis of the convective terms, which includes the solution of the associated Riemann problem, enables to propose a standard projection scheme, and to get rid of spurious oscillations.

Photoelectron angular distributions (PADs) produced from the photoionization of chiral molecules using elliptically polarized light exhibit a forward/backward asymmetry with respect to the optical propagation direction. By recording these distributions using the velocity-map imaging (VMI) technique, the resulting photoelectron elliptical dichroism (PEELD) has previously been demonstrated as a promising spectroscopic tool for studying chiral molecules in the gas phase. The use of elliptically polarized laser pulses, however, produces PADs (and consequently, PEELD distributions) that do not exhibit cylindrical symmetry about the propagation axis. This leads to significant limitations and challenges when employing conventional VMI acquisition and data processing strategies. Using novel photoelectron image analysis methods based around Hankel transform reconstruction tomography and machine learning, however, we have quantified—for the first time—significant symmetry-breaking contributions to PEELD signals that are of a comparable magnitude to the symmetric terms in the multiphoton ionization of (1R,4R)-(+)- and (1S,4S)-(−)-camphor. This contradicts any assumptions that symmetry-breaking can be ignored when reconstructing VMI data. Furthermore, these same symmetry-breaking terms are expected to appear in any experiment where circular and linear laser fields are used together. This ionization scheme is particularly relevant for investigating dynamics in chiral molecules, but it is not limited to them. Developing a full understanding of these terms and the role they play in the photoionization of chiral molecules is of clear importance if the potential of PEELD and related effects for future practical applications is to be fully realized. [ABSTRACT FROM AUTHOR]

Abstract Breast cancer is a common malignancy and a leading cause of cancer-related deaths in women worldwide. Its early diagnosis can significantly reduce the morbidity and mortality rates in women. To this end, histopathological diagnosis is usually followed as the gold standard approach. However, this process is tedious, labor-intensive, and may be subject to inter-reader variability. Accordingly, an automatic diagnostic system can assist to improve the quality of diagnosis. This paper presents a deep learning approach to automatically classify hematoxylin-eosin-stained breast cancer microscopy images into normal tissue, benign lesion, in situ carcinoma, and invasive carcinoma using our collected dataset. Our proposed model exploited six intermediate layers of the Xception (Extreme Inception) network to retrieve robust and abstract features from input images. First, we optimized the proposed model on the original (unnormalized) dataset using 5-fold cross-validation. Then, we investigated its performance on four normalized datasets resulting from Reinhard, Ruifrok, Macenko, and Vahadane stain normalization. For original images, our proposed framework yielded an accuracy of 98% along with a kappa score of 0.969. Also, it achieved an average AUC-ROC score of 0.998 as well as a mean AUC-PR value of 0.995. Specifically, for in situ carcinoma and invasive carcinoma, it offered sensitivity of 96% and 99%, respectively. For normalized images, the proposed architecture performed better for Makenko normalization compared to the other three techniques. In this case, the proposed model achieved an accuracy of 97.79% together with a kappa score of 0.965. Also, it attained an average AUC-ROC score of 0.997 and a mean AUC-PR value of 0.991. Especially, for in situ carcinoma and invasive carcinoma, it offered sensitivity of 96% and 99%, respectively. These results demonstrate that our proposed model outperformed the baseline AlexNet as well as state-of-the-art VGG16, VGG19, Inception-v3, and Xception models with their default settings. Furthermore, it can be inferred that although stain normalization techniques offered competitive performance, they could not surpass the results of the original dataset.

The microbiome has shown a correlation with the diet and lifestyle of each population in health and disease, the ability to communicate at the cellular level with the host through innate and adaptative immune receptors, and therefore an important role in modulating inflammatory process related to the establishment and progression of cancer. The oral cavity is one of the most important interaction windows between the human body and the environment, allowing the entry of an important number of microorganisms and their passage across the gastrointestinal tract and lungs. In this review, the contribution of the microbiome network to the establishment of systemic diseases like cancer is analyzed through their synergistic interactions and bidirectional crosstalk in the oral-gut-lung axis as well as its communication with the host cells. Moreover, the impact of the characteristic microbiota of each population in the formation of the multiomics molecular metafirm of the oral-gut-lung axis is also analyzed through state-of-the-art sequencing techniques, which allow a global study of the molecular processes involved of the flow of the microbiota environmental signals through cancer-related cells and its relationship with the establishment of the transcription factor network responsible for the control of regulatory processes involved with tumorigenesis.

Background: The diagnosis and treatment of lung, colon, and gastric cancer through the histologic characteristics and genomic biomarkers have not had a strong impact on the mortality rates of the top three global causes of death by cancer. Methods: Twenty-five transcriptomic analyses (10 lung cancer, 10 gastric cancer, and 5 colon cancer datasets) followed our own bioinformatic pipeline based on the utilization of specialized libraries fromthe R language and DAVID´s gene enrichment analyses to identify a regulatory metafirm network of transcription factors and target genes common in every type of cancer, with experimental evidence that supports its relationship with the unlocking of cell phenotypic plasticity for the acquisition of the hallmarks of cancer during the tumoral process. The network's regulatory functional and signaling pathways might depend on the constant crosstalk with the microbiome network established in the oral-gut-lung axis. Results: The global transcriptomic network analysis highlighted the impact of transcription factors (SOX4, TCF3, TEAD4, ETV4, and FOXM1) that might be related to stemcell programming and cancer progression through the regulation of the expression of genes, such as cancer-cell membrane receptors, that interact with several microorganisms, including human T-cell leukemia virus 1 (HTLV-1), the human papilloma virus (HPV), the Epstein--Barr virus (EBV), and SARS-CoV-2. These interactions can trigger the MAPK, non-canonicalWNT, and IFN signaling pathways, which regulate key transcription factor overexpression during the establishment and progression of lung, colon, and gastric cancer, respectively, along with the formation of the microbiome network. Conclusion: The global transcriptomic network analysis highlights the important interaction between key transcription factors in lung, colon, and gastric cancer, which regulates the expression of cancer-cell membrane receptors for the interaction with the microbiome network during the tumorigenic process. [ABSTRACT FROM AUTHOR]

Background: Mobility restrictions prevent the spread of infections to disease-free areas, and early in the coronavirus disease 2019 (COVID-19) pandemic, most countries imposed severe restrictions on mobility as soon as it was clear that containment of local outbreaks was insufficient to control spread. These restrictions have adverse impacts on the economy and other aspects of human health, and it is important to quantify their impact for evaluating their future value. Methods: Here we develop Scotland Coronavirus transmission Model (SCoVMod), a model for COVID-19 in Scotland, which presents unusual challenges because of its diverse geography and population conditions. Our fitted model captures spatio-temporal patterns of mortality in the first phase of the epidemic to a fine geographical scale. Results: We find that lockdown restrictions reduced transmission rates down to an estimated 12\% of its pre-lockdown rate. We show that, while the timing of COVID-19 restrictions influences the role of the transmission rate on the number of COVID-related deaths, early reduction in long distance movements does not. However, poor health associated with deprivation has a considerable association with mortality; the Council Area (CA) with the greatest health-related deprivation was found to have a mortality rate 2.45 times greater than the CA with the lowest health-related deprivation considering all deaths occurring outside of carehomes. Conclusions: We find that in even an early epidemic with poor case ascertainment, a useful spatially explicit model can be fit with meaningful parameters based on the spatio-temporal distribution of death counts. Our simple approach is useful to strategically examine trade-offs between travel related restrictions and physical distancing, and the effect of deprivation-related factors on outcomes.

Abstract Introduction The objective of this study is to show the importance of interspecies links and temporal network dynamics of a multi-species livestock movement network. Although both cattle and sheep networks have been previously studied, cattle-sheep multi-species networks have not generally been studied in-depth. The central question of this study is how the combination of cattle and sheep movements affects the potential for disease spread on the combined network. Materials and methods Our analysis considers static and temporal representations of networks based on recorded animal movements. We computed network-based node importance measures of two single-species networks, and compared the top-ranked premises with the ones in the multi-species network. We propose the use of a measure based on contact chains calculated in a network weighted with transmission probabilities to assess the importance of premises in an outbreak. To ground our investigation in infectious disease epidemiology, we compared this suggested measure with the results of disease simulation models with asymmetric probabilities of transmission between species. Results Our analysis of the temporal networks shows that the premises which are likely to drive the epidemic in this multi-species network differ from the ones in both the cattle and the sheep networks. Although sheep movements are highly seasonal, the estimated size of an epidemic is significantly larger in the multi-species network than in the cattle network, independently of the period of the year. Finally, we demonstrate that a measure based on contact chains allow us to identify around 30% of the key farms in a simulated epidemic, ignoring markets, whilst static network measures identify less than 10% of these farms. Conclusion Our results ascertain the importance of combining species networks, as well as considering layers of temporal livestock movements in detail for the study of disease spread.

This article discusses a case study of a patient with an unusual form of hypobetalipoproteinemia (HBL), a condition characterized by low levels of LDL cholesterol. The patient's clinical features and lipid profile did not align, leading to further investigation using next-generation sequencing. The study identified rare mutations in the DHCR7 gene, which is associated with Smith Lemli Opitz syndrome (SLOS). SLOS is a rare autosomal recessive disorder that affects cholesterol metabolism. The diagnosis of SLOS prompted dietary cholesterol supplementation and genetic counseling for the family. This case highlights the importance of combining genetic testing and biochemical investigations to accurately diagnose and treat rare dyslipidemia disorders. [Extracted from the article]

Copy number variants (CNVs) remain a major etiological cause of neurodevelopmental delay and congenital malformations. Chromosomal microarray analysis (CMA) represents the gold standard for CNVs molecular characterization. We applied CMA throughout the patient's clinical diagnostic workup, as the patient's medical provider requested. We collected CMA results of 3380 patients enrolled for 5 years (2016–2021). We found 830 CNVs in 719 patients with potential clinical significance, that is, (i) pathogenic, (ii) likely pathogenic, and (iii) variants of uncertain significance (VUS), from which 10.6% (predominantly involving chromosomes 15 and 22) were most likely the final cause underpinning the patients' clinical phenotype. For those associated with neurodevelopmental phenotypes, the rate of pathogenic or likely pathogenic findings among the patients with CNVs was 60.75%. When considering epileptic phenotypes, it was 59%. Interestingly, our protocol identified two gains harbored in 17q21.31 and 9q34.3, internationally classified initially as VUS. However, because of their high frequency, we propose that these two VUS be reclassified as likely benign in this widely heterogeneous phenotypic population. These results support the diagnostic yield efficiency of CMA in characterizing CNVs to define the final molecular cause of genetic diseases in this cohort of Colombian patients, the most significant sample of patients from a Latino population, and define new benign polymorphic CNVs. [ABSTRACT FROM AUTHOR]

A whole-exome capture and next-generation sequencing was applied to an 11 y/o patient with a clinical history of congenital hypotonia, generalized motor and cognitive neurodevelopmental delay, and severe cognitive deficit, and without any identifiable Syndromic pattern, and to her parents, we disclosed a de novo heterozygous pathogenic mutation, c.697_699del p.Phe233del (rs786204835)(ACMG classification PS2, PM1, PM2, PP5), harbored in the PURA gene (MIM*600473) (5q31.3), associated with Autosomal Dominant Mental Retardation 31 (MIM # 616158). We used the significant improvement in the accuracy of protein structure prediction recently implemented in AlphaFold that incorporates novel neural network architectures and training procedures based on the evolutionary, physical, and geometric constraints of protein structures. The wild-type (WT) sequence and the mutated sequence, missing the Phe233, were reconstructed. The predicted local Distance Difference Test (lDDT) for the PURAwt and the PURA–Phe233del showed that the occurrence of the Phe233del affects between 220–320 amino acids. The distortion in the PURA structural conformation in the ~5 Å surrounding area after the p.Phe233del produces a conspicuous disruption of the repeat III, where the DNA and RNA helix unwinding capability occurs. PURA Protein–DNA docking corroborated these results in an in silico analysis that showed a loss of the contact of the PURA–Phe233del III repeat domain model with the DNA. Together, (i) the energetic and stereochemical, (ii) the hydropathic indexes and polarity surfaces, and (iii) the hybrid Quantum Mechanics–Molecular Mechanics (QM–MM) analyses of the PURA molecular models demarcate, at the atomic resolution, the specific surrounding region affected by these mutations and pave the way for future cell-based functional analysis. To the best of our knowledge, this is the first report of a de novo mutation underpinning a PURA syndrome in a Latin American patient and highlights the importance of predicting the molecular effects in protein structure using artificial intelligence algorithms and molecular and atomic resolution stereochemical analyses.

Mosquito-borne flaviviruses with an enzootic transmission cycle like Japanese encephalitis virus (JEV) and West Nile virus (WNV) are a major public health concern. The circulation of JEV in Southeast Asia is well-documented, and the important role of pigs as amplification hosts for the virus is long known. The influence of other domestic animals especially poultry that lives in high abundance and close proximity to humans is not intensively analyzed. Another understudied field in Asia is the presence of the closely related WNV. Such analyses are difficult to perform due to the intense antigenic cross-reactivity between these viruses and the lack of suitable standardized serological assays. The main objective of this study was to assess the prevalence of JEV and WNV flaviviruses in domestic birds, detailed in chickens and ducks, in three different Cambodian provinces. We determined the flavivirus seroprevalence using an hemagglutination inhibition assay (HIA). Additionally, we investigated in positive samples the presence of JEV and WNV neutralizing antibodies (nAb) using foci reduction neutralization test (FRNT). We found 29% (180/620) of the investigated birds positive for flavivirus antibodies with an age-depended increase of the seroprevalence (OR = 1.04) and a higher prevalence in ducks compared to chicken (OR = 3.01). Within the flavivirus-positive birds, we found 43% (28/65) with nAb against JEV. We also observed the expected cross-reactivity between JEV and WNV, by identifying 18.5% double-positive birds that had higher titers of nAb than single-positive birds. Additionally, seven domestic birds (10.7%) showed only nAb against WNV and no nAb against JEV. Our study provides evidence for an intense JEV circulation in domestic birds in Cambodia, and the first serological evidence for WNV presence in Southeast Asia since decades. These findings mark the need for a re-definition of areas at risk for JEV and WNV transmission, and the need for further and intensified surveillance of mosquito-transmitted diseases in domestic animals.

Peste des petits ruminants virus (PPRV), responsible for peste des petits ruminants (PPR), is widely circulating in Africa and Asia. The disease is a huge burden for the economy and development of the affected countries. In Eastern Africa, the disease is considered endemic. Because of the geographic proximity and existing trade between eastern African countries and the Comoros archipelago, the latter is at risk of introduction and spread, and the first PPR outbreaks occurred in the Union of the Comoros in 2012. The objective of this study was to map the areas suitable for PPR occurrence and spread in the Union of the Comoros and four eastern African countries, namely Ethiopia, Uganda, Kenya, and Tanzania. A Geographic Information System (GIS)-based Multicriteria Evaluation (MCE) was developed. Risk factors for PPR occurrence and spread, and their relative importance, were identified using literature review and expert-based knowledge. Corresponding geographic data were collected, standardized, and combined based on a weighted linear combination to obtain PPR suitability maps. The accuracy of the maps was assessed using outbreak data from the EMPRES database and a ROC curve analysis. Our model showed an excellent ability to distinguish between absence and presence of outbreaks in Eastern Africa (AUC = 0.907; 95% CI [0.820–0.994]), and a very good performance in the Union of the Comoros (AUC = 0.889, 95% CI: [0.694–1]). These results highlight the efficiency of the GIS-MCE method, which can be applied at different geographic scales: continental, national and local. The resulting maps provide decision support tools for implementation of disease surveillance and control measures, thus contributing to the PPR eradication goal of OIE and FAO by 2030.