Evaluation of fecal smear methods for research on Cryptosporidium spp. oocysts in the feces of dairy calves

The objective of this study is to compare the direct fecal smear (DFS) and centrifugal sedimentation (CS) methods in the detection of Cryptosporidium spp. oocysts in fecal samples of dairy calves. One hundred and fourteen fecal samples were collected from calves aged up to six months from 10 dairy farms located in Palotina and Francisco Alves, Paraná, Brazil. The microscopic analysis revealed the presence of Cryptosporidium spp. oocysts in 51.75% (59/114) of the samples in both methods. In CS, 48.25% (55/114) of the samples were positive, while in DFS slides, only 6.14% (7/114) were positive. Only 4 samples were positive exclusively in DFS. To ensure that there were no false-negative results in the microscopic analysis, the 55 samples that were negative in both DFS and CS were selected for molecular analysis using the nested PCR (nPCR). Of these 55 samples, 24% (13/55) were positive and forwarded for sequencing part of the genome, which made it possible to identify C. parvum, C. bovis and C. ryanae. Besides the characterization of the Cryptosporidium species, it was possible to identify bacteria of the genus Acinetobacter interfering directly in the analyzed samples. The microscopic analysis also revealed higher sensitivity when CS was used to make the fecal smears. However, some samples that were negative in this technique had positive PCR results. Thus, molecular analysis is indicated to confirm cases of Cryptosporidium spp. Further studies are necessary to prove the specificities of the used primers since the results obtained in nPCR were positive for the protozoan but, when genetic sequencing was performed, Acinetobacter spp. was identified.

Deep learning model for distributed denial of service (DDoS) detection

Distributed denial of service (DDoS) attacks is one of the serious threats in the domain of cybersecurity where it affects the availability of online services by disrupting access to its legitimate users. The consequences of such attacks could be millions of dollars in worth since all of the online services are relying on high availability. The magnitude of DDoS attacks is ever increasing as attackers are smart enough to innovate their attacking strategies to expose vulnerabilities in the intrusion detection models or mitigation mechanisms. The history of DDoS attacks reflects that network and transport layers of the OSI model were the initial target of the attackers, but the recent history from the cybersecurity domain proves that the attacking momentum has shifted toward the application layer of the OSI model which presents a high degree of difficulty distinguishing the attack and benign traffics that make the combat against application-layer DDoS attack a sophisticated task. Striding for high accuracy with high DDoS classification recall is key for any DDoS detection mechanism to keep the reliability and trustworthiness of such a system. In this paper, a deep learning approach for application-layer DDoS detection is proposed by using an autoencoder to perform the feature selection and Deep neural networks to perform the attack classification. A popular benchmark dataset CIC DoS 2017 is selected by extracting the most appealing features from the packet flows. The proposed model has achieved an accuracy of 99.83% with a detection rate of 99.84% while maintaining the false-negative rate of 0.17%, which has the heights accuracy rate among the literature reviewed so far.

Efficacy of Fluorecare SARS-CoV-2 Spike Protein Test Kit for SARS-CoV-2 detection in nasopharyngeal samples of 121 individuals working in a manufacturing company

Objectives The aim of this study was to evaluate the clinical performance of the Fluorecare SARS-CoV-2 Spike Protein Test Kit, a rapid immunochromatographic assay for SARS-CoV-2 detection. Moreover, we sought to point out the strategy adopted by a local company to lift the lockdown without leading to an increase in the number of COVID-19 cases, by performing a precise and timely health surveillance. Methods The rapid Fluorecare SARS-CoV-2 Spike Protein Test was performed immediately after sampling following the manufacturer’s instructions. RT-PCRs were performed within 24 hours of specimen collection. A total amount of 253 nasopharyngeal samples from 121 individuals were collected between March 16 and April 2, 2021 and tested. Results Of 253 nasopharyngeal samples, 11 (9.1%) were positive and 242 (90.9%) were negative for SARS-CoV-2 RNA by RT-PCR assays. The rapid SARS-CoV-2 antigen detection test’s mean sensitivity and specificity were 84,6% (95% CI, 54.6–98.1%) and 100% (95% CI, 98.6–100%), respectively. Two false negative test results were obtained from samples with high RT-PCR cycle threshold (Ct). Conclusion Our study suggested that Fluorecare SARS-CoV-2 Spike Protein Test can be introduced into daily diagnostic practice, as its mean sensitivity and specificity follow the standards recommended by WHO and IFCC Task Force. In addition, we underlined how the strategy adopted by a local company to risk assessment and health surveillance was appropriate for infection containment. This real-life scenario gave us the possibility to experience potential approaches aimed to preserve public health and work activities.

A 2-Gene Host Signature for Improved Accuracy of COVID-19 Diagnosis Agnostic to Viral Variants

The continued emergence of SARS-CoV-2 variants is one of several factors that may cause false negative viral PCR test results. Such tests are also susceptible to false positive results due to trace contamination from high viral titer samples. Host immune response markers provide an orthogonal indication of infection that can mitigate these concerns when combined with direct viral detection. Here, we leverage nasopharyngeal swab RNA-seq data from patients with COVID-19, other viral acute respiratory illnesses and non-viral conditions (n=318) to develop support vector machine classifiers that rely on a parsimonious 2-gene host signature to predict COVID-19. Optimal classifiers achieve an area under the receiver operating characteristic curve (AUC) greater than 0.9 when evaluated on an independent RNA-seq cohort (n=553). We show that a classifier relying on a single interferon-stimulated gene, such as IFI6 or IFI44, measured in RT-qPCR assays (n=144) achieves AUC values as high as 0.88. Addition of a second gene, such as GBP5, significantly improves the specificity compared to other respiratory viruses. The performance of a clinically practical 2-gene RT-qPCR classifier is robust across common SARS-CoV-2 variants, including Omicron, and is unaffected by cross-contamination, demonstrating its utility for improving accuracy of COVID-19 diagnostics.

Gait instability and estimated core temperature predict exertional heat stroke

ObjectiveExertional heat stroke (EHS), characterised by a high core body temperature (Tcr) and central nervous system (CNS) dysfunction, is a concern for athletes, workers and military personnel who must train and perform in hot environments. The objective of this study was to determine whether algorithms that estimate Tcr from heart rate and gait instability from a trunk-worn sensor system can forward predict EHS onset.MethodsHeart rate and three-axis accelerometry data were collected from chest-worn sensors from 1806 US military personnel participating in timed 4/5-mile runs, and loaded marches of 7 and 12 miles; in total, 3422 high EHS-risk training datasets were available for analysis. Six soldiers were diagnosed with heat stroke and all had rectal temperatures of >41°C when first measured and were exhibiting CNS dysfunction. Estimated core temperature (ECTemp) was computed from sequential measures of heart rate. Gait instability was computed from three-axis accelerometry using features of pattern dispersion and autocorrelation.ResultsThe six soldiers who experienced heat stroke were among the hottest compared with the other soldiers in the respective training events with ECTemps ranging from 39.2°C to 40.8°C. Combining ECTemp and gait instability measures successfully identified all six EHS casualties at least 3.5 min in advance of collapse while falsely identifying 6.1% (209 total false positives) examples where exertional heat illness symptoms were neither observed nor reported. No false-negative cases were noted.ConclusionThe combination of two algorithms that estimate Tcr and ataxic gate appears promising for real-time alerting of impending EHS.

The mutationathon highlights the importance of reaching standardization in estimates of pedigree-based germline mutation rates

In the past decade, several studies have estimated the human per-generation germline mutation rate using large pedigrees. More recently, estimates for various non-human species have been published. However, methodological differences among studies in detecting germline mutations and estimating mutation rates make direct comparisons difficult. Here, we describe the many different steps involved in estimating pedigree-based mutation rates, including sampling, sequencing, mapping, variant calling, filtering, and how to appropriately account for false-positive and false-negative rates. For each step, we review the different methods and parameter choices that have been used in the recent literature. Additionally, we present the results from a 'Mutationathon', a competition organized among five research labs to compare germline mutation rate estimates for a single pedigree of rhesus macaques. We report almost a two-fold variation in the final estimated rate among groups using different post-alignment processing, calling, and filtering criteria and provide details into the sources of variation across studies. Though the difference among estimates is not statistically significant, this discrepancy emphasizes the need for standardized methods in mutation rate estimations and the difficulty in comparing rates from different studies. Finally, this work aims to provide guidelines for computational and statistical benchmarks for future studies interested in identifying germline mutations from pedigrees.

Detection of Low-Copy Human Virus DNA upon Prolonged Formalin Fixation

Formalin fixation, albeit an outstanding method for morphological and molecular preservation, induces DNA damage and cross-linking, which can hinder nucleic acid screening. This is of particular concern in the detection of low-abundance targets, such as persistent DNA viruses. In the present study, we evaluated the analytical sensitivity of viral detection in lung, liver, and kidney specimens from four deceased individuals. The samples were either frozen or incubated in formalin (±paraffin embedding) for up to 10 days. We tested two DNA extraction protocols for the control of efficient yields and viral detections. We used short-amplicon qPCRs (63–159 nucleotides) to detect 11 DNA viruses, as well as hybridization capture of these plus 27 additional ones, followed by deep sequencing. We observed marginally higher ratios of amplifiable DNA and scantly higher viral genoprevalences in the samples extracted with the FFPE dedicated protocol. Based on the findings in the frozen samples, most viruses were detected regardless of the extended fixation times. False-negative calls, particularly by qPCR, correlated with low levels of viral DNA (<250 copies/million cells) and longer PCR amplicons (>150 base pairs). Our data suggest that low-copy viral DNAs can be satisfactorily investigated from FFPE specimens, and encourages further examination of historical materials.

Machine learned calibrations to high-throughput molecular excited state calculations

Understanding the excited state properties of molecules provides insights into how they interact with light. These interactions can be exploited to design compounds for photochemical applications, including enhanced spectral conversion of light to increase the efficiency of photovoltaic cells. While chemical discovery is time- and resource-intensive experimentally, computational chemistry can be used to screen large-scale databases for molecules of interest in a procedure known as high-throughput virtual screening. The first step usually involves a high-speed but low-accuracy method to screen large numbers of molecules (potentially millions) so only the best candidates are evaluated with expensive methods. However, use of a coarse first-pass screening method can potentially result in high false positive or false negative rates. Therefore, this study uses machine learning to calibrate a high-throughput technique (xTB-sTDA) against a higher accuracy one (TD-DFT). Testing the calibration model shows a ~5-fold decrease in error in-domain and a ~3-fold decrease out-of-domain. The resulting mean absolute error of ~0.14 eV is in line with previous work in machine learning calibrations and out-performs previous work in linear calibration of xTB-sTDA. We then apply the calibration model to screen a 250k molecule database and map inaccuracies of xTB-sTDA in chemical space. We also show generalizability of the workflow by calibrating against a higher-level technique (CC2), yielding a similarly low error. Overall, this work demonstrates machine learning can be used to develop a both cheap and accurate method for large-scale excited state screening, enabling accelerated molecular discovery across a variety of disciplines.

Ratiometric Electrochemical Biosensor Based on Internally Controlled Duplex PCR for Detection of Mycobacterium Tuberculosis

The pathogenic bacteria Mycobacterium tuberculosis (MTB) is responsible for tuberculosis, which is well known as the globally leading cause of death. The likelihood of false negative interpretation as well as potential influence from intrinsic and extrinsic factors are considerably minimized by the incorporation of internal control (IC) detection in the developed assay platform. Ratiometric electrochemical (REC) biosensor for detection of MTB was developed based on the IC integration via duplex PCR (dPCR) and a dual-signal electrochemical readout. The MTB- or IC-specific PNA probe was labeled with methylene blue (MB) or ferrocene (FC), respectively at the C terminus, producing a strong square wave voltammetry signal. Interaction of the ICdPCR product could induce changes in the dynamics of these two redox-labeled PNA probes (MTB-MB and IC-FC) that were attached to the screen-printed gold electrode via formation of a self-assembled monolayer. Using this MB as a reporter and FC as an IC, the REC ICdPCR biosensor achieved a broad detection range from 10 fM to 10 nM and a detection limit of 1.26 fM, corresponding to approximately 2.5 bacteria cells. The REC ICdPCR biosensor was applied to MTB measurement in practical samples, exhibiting high accuracy and more importantly high practicability.

Use of Artificial Intelligence to Triage Patients with Flu-Like Symptoms Using Imaging in Non-COVID-19 Hospitals during COVID-19 Pandemic: An Ongoing 8-Month Experience

Background Evaluation of suspected coronavirus disease-2019 (COVID-19) patient is a diagnostic dilemma as it commonly presents like influenza in early stages. Studies and guidelines have emerged both for and against the use of imaging as a frontline tool to investigate such patients. Reverse transcriptase-polymerase chain reaction (RT-PCR) is suggested as the backbone of diagnosis. We designed and tested a diagnostic algorithm using artificial intelligence (AI) to determine the role of imaging in the evaluation of patients with acute flu-like presentation. Materials and Methods Overall, 3,235 consecutive patients with flu-like presentation were evaluated over a period of 240 days. All patients underwent plain radiographs of chest with computer-aided detection for COVID-19 (CAD4COVID) AI analysis. Based on the threshold scores, they were divided into two groups: group A (score < 50) and group B (score > 50). Group A patients were discharged and put on routine symptomatic treatment and follow-up with RT-PCR, while group B patients underwent high-resolution computed tomography (HRCT) followed by COVID-19 AI analysis and RT-PCR test. These were then triaged into COVID-19 and non-COVID-19 subgroups based on COVID-19 similarity scores by AI, and lung severity scores were also determined. Results Group A had 2,209 (68.3%) patients with CAD4COVID score of <50 while 1,026 (31.7%) patients comprised group B. Also, 825 (25.5%) patients were COVID-19 positive with COVID-19 similarity threshold of >0.85 on AI. RT-PCR was positive in 415 and false-negative in 115 patients while 12 patients died before the test could be done. The sensitivity and specificity of CAD4COVID AI analysis on plain radiographs for detection of any lung abnormality combined with HRCT AI analysis was 97.9% and 99% using the above algorithm. Conclusion Combined use of chest radiographs and plain HRCT with AI-based analysis is useful and an accurate frontline tool to triage patients with acute flu-like symptoms in non-COVID-19 health care facilities.