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Enterobacteriaceae pathogens such as Escherichia coli, Salmonella sp., Shigella sp., Proteus sp., and Klebsiella pneumoniae cause a wide range of gastrointestinal and other mucosal infections. These bacteria acquire antibiotic resistance very quickly and evolve into multi-drug resistant strains thereby making the treatment very difficult. The outer membrane proteins (OMPs) in Enterobacteriaceae are potential vaccine candidates owing for their high immunogenicity and amino acid conservation. The OmpA is one such protein which need to be investigated for the development of a potential subunit vaccine against multiple infections casued by the pathogens of Enterobacteriaceae. To investigate this, we expressed and purified the highly conserved OmpA of S. typhimurium and studied the antibody mediated cross reactivity with the other Enterobacteriaceae pathogens. This was validated through dot ELISA performed with the hyperimmune sera raised against rOmpA of S. typhimurium. We further analyzed the sequence of OmpA protein and clearly understood that the B-cell epitopes in the protein are highly conserved are responsible for cross reactivity among the Enterobacteriaceae pathogens. This work led to findings that provide strong evidence for the application of OmpA in broad-spectrum subunit vaccine against enteric infections.

Cyclophosphamide (CP) is an alkylating agent commonly included in multi-drug treatment protocols for canine cancer. As a prodrug, CP requires hepatic metabolism for activation to the intermediate compound 4-hydroxycyclophosphamide (4-OHCP) which then spontaneously forms alkylating phosphoramide mustard. CP is frequently administered in a fractionated manner, with the total dose given over multiple days. CP is reported to cause auto-induction of metabolism in humans, with faster CP clearance and relatively increased 4-OHCP formation following fractionated versus bolus dosing, however canine pharmacokinetic studies of CP dose fractionation are lacking. The study objective was to evaluate the pharmacokinetics of fractionated oral CP dosing at a dose of 200-250 mg/m2 over 3 to 4 days in a prospectively identified population of cancer-bearing dogs. Plasma concentrations of CP and 4-OHCP were measured by ultra-high performance liquid chromatography tandem-mass spectrometry in eight dogs following the first and last doses to assess for auto-induction of CP metabolism. No significant difference in the rate of CP elimination between first and last doses were detected (0.73 ± 0.46 vs. 1.22 ± 0.5 h-1 ; p = .125). Additionally, no significant difference in dose-normalized 4-OHCP exposure was identified between first and last doses (5.9 ± 2.1 vs. 7.9 ± 6.4 h × ng/ml; p = .936). These results suggest that fractionated dosing may not increase exposure to the active metabolite of CP in dogs as it does in humans. As such, standard bolus dosing and fractionated dosing may be equivalent in terms of bio-activation of CP in dogs administered a dose of 200-250 mg/m2 .

Advancements in network science have facilitated the study of brain communication networks. Existing techniques for identifying event-related brain functional networks (BFNs) often result in fully connected networks. However, determining the optimal and most significant network representation for event-related BFNs is crucial for understanding complex brain networks. The presence of both false and genuine connections in the fully connected network requires network thresholding to eliminate false connections. However, a generalized framework for thresholding in network neuroscience is currently lacking. To address this, we propose four novel methods that leverage network properties, energy, and efficiency to select a generalized threshold level. This threshold serves as the basis for identifying the optimal and most significant event-related BFN. We validate our methods on an openly available emotion dataset and demonstrate their effectiveness in identifying multiple events. Our proposed approach can serve as a versatile thresholding technique to represent the fully connected network as an event-related BFN.

Diagnosing and treating dementia, including mild cognitive impairment (MCI), is challenging due to diverse disease types and overlapping symptoms. Early MCI detection is vital as it can precede dementia, yet distinguishing it from later stage dementia is intricate due to subtle symptoms. The primary objective of this study is to adopt a complex network perspective to unravel the underlying pathophysiological mechanisms of dementia-related disorders. Leveraging the extensive availability of electroencephalogram (EEG) data, our study focuses on the meticulous identification and analysis of EEG-based brain functional network (BFNs) associated with dementia-related disorders. To achieve this, we employ the Phase Lag Index (PLI) as a connectivity measure, offering a comprehensive view of neural interactions. To enhance the analytical rigor, we introduce a data-driven threshold selection technique. This innovative approach allows us to compare the topological structures of the formulated BFNs using complex network measures quantitatively and statistically. Furthermore, we harness the power of these BFNs by utilizing them as pre-defined graph inputs for a Graph Convolution Network (GCN-net) based approach. The results demonstrate that graph theory metrics, such as the rich-club coefficient, transitivity, and assortativity coefficients, effectively distinguish between MCI, Alzheimer’s disease (AD) and vascular dementia (VD). Furthermore, GCN-net achieves high accuracy (95.07% delta, 80.62% theta) and F1-scores (0.92 delta, 0.67 theta), highlighting the effectiveness of EEG-based BFNs in the analysis of dementia-related disorders.

A 3-year-old dog was referred to the Veterinary Medical Teaching Hospital of the University of California-Davis for further evaluation of episodes of epistaxis of 1-year duration and peripheral lymphadenopathy. The patient had a history of atopic dermatitis with no travel history outside of California. Hyperglobulinemia with a polyclonal gammopathy was noted on serum protein electrophoresis. Microscopic evaluation of a bone marrow aspirate sample revealed many free and intra-cellular amastigotes of Leishmania sp. that was further confirmed by qPCR as L infantum. This is, to the best of our knowledge, the first reported case of canine leishmaniasis in the state of California. The patient is believed to have been vertically infected from the dam who is from Serbia and remained subclinical until presentation. Because the clinical progression of leishmaniasis is variable, it is important that precautions be discussed with owners acquiring puppies with dams from endemic regions of leishmaniasis to prevent zoonotic exposure in states where competent vectors are present.

Abstract Background Throughout the first year of the COVID-19 pandemic, our research team monitored and documented policy changes in United States (U.S.) prison systems. Data sources included prison websites and official prison social media accounts. Over 2500 data sources relevant to the COVID-19 pandemic in U.S. prisons were located and summarized in to five different categories: 1) prevention, 2) case identification and intervention, 3) movement, 4) social communication and connection, and 5) programming, recreation, and privileges. Results All state prison systems reportedly enacted multiple policies intended to limit the spread of COVID-19 during the pandemic. Document analysis revealed that the most commonly released policies were restrictions on social contacts and privileges, basic preventive measures (e.g., distribution of masks), and basic case identification measures (e.g., verbal screening and temperature checks). Utilization of social media for policy communication varied significantly across states, though relevant data was more often released on Facebook than Twitter. Conclusions Together, our work provides foundational knowledge on the wide breadth of policies that were reportedly enacted in the first year of the pandemic that may be used as a base for quantitative work on policy effectiveness and examinations of implementation.

The precise prediction of tumor motion for radiotherapy has proven challenging due to the non-stationary nature of respiration-induced motion, frequently accompanied by unpredictable irregularities. Despite the availability of numerous prediction methods for respiratory motion prediction, the prediction errors they generate often suffer from large prediction horizons, intra-trace variabilities, and irregularities. To overcome these challenges, we have employed a hybrid method, which combines empirical mode decomposition (EMD) and random vector functional link (RVFL), referred to as EMD-RVFL. In the initial stage, EMD is used to decompose respiratory motion into interpretable intrinsic mode functions (IMFs) and residue. Subsequently, the RVFL network is trained for each obtained IMF and residue. Finally, the prediction results of all the IMFs and residue are summed up to obtain the final predicted output. We validated this proposed method on the benchmark datasets of 304 respiratory motion traces obtained from 31 patients for various prediction lengths, which are equivalent to the latencies of radiotherapy systems. In direct comparison with existing prediction techniques, our hybrid architecture consistently delivers a robust and highly accurate prediction performance. This proof-of-concept study indicates that the proposed approach is feasible and has the potential to improve the accuracy and effectiveness of radiotherapy treatment.

BackgroundDementia-related disorders have been an age-long challenge to the research and healthcare communities as their various forms are expressed with similar clinical symptoms. These disorders are usually irreversible at their late onset, hence their lack of validated and approved cure. Since their prodromal stages usually lurk for a long period of time before the expression of noticeable clinical symptoms, a secondary prevention which has to do with treating the early onsets has been suggested as the possible solution. Connectivity analysis of electrophysiology signals has played significant roles in the diagnosis of various dementia disorders through early onset identification.ObjectiveWith the various applications of electrophysiology signals, the purpose of this study is to systematically review the step-by-step procedures of connectivity analysis frameworks for dementia disorders. This study aims at identifying the methodological issues involved in such frameworks and also suggests approaches to solve such issues.MethodsIn this study, ProQuest, PubMed, IEEE Xplore, Springer Link, and Science Direct databases are employed for exploring the evolution and advancement of connectivity analysis of electrophysiology signals of dementia-related disorders between January 2016 to December 2022. The quality of assessment of the studied articles was done using Cochrane guidelines for the systematic review of diagnostic test accuracy.ResultsOut of a total of 4,638 articles found to have been published on the review scope between January 2016 to December 2022, a total of 51 peer-review articles were identified to completely satisfy the review criteria. An increasing trend of research in this domain is identified within the considered time frame. The ratio of MEG and EEG utilization found within the reviewed articles is 1:8. Most of the reviewed articles employed graph theory metrics for their analysis with clustering coefficient (CC), global efficiency (GE), and characteristic path length (CPL) appearing more frequently compared to other metrics.SignificanceThis study provides general insight into how to employ connectivity measures for the analysis of electrophysiology signals of dementia-related disorders in order to better understand their underlying mechanism and their differential diagnosis.

Abstract Background While rare, multiple individual case reports have described mixed thyroid tumours in dogs containing both epithelial and mesenchymal neoplastic components. Objectives In this retrospective case series, we describe the clinical presentation, treatment and outcome of 14 dogs of canine thyroid tumours with concurrent mesenchymal and epithelial neoplastic populations. Methods Fourteen cases were retrospectively abstracted from nine institutions. Histopathologic samples and reports were collected from 10/14 dogs and reviewed by a single board‐certified anatomic pathologist. Results All 14 dogs had curative‐intent surgery to remove the thyroid neoplasm. The most common surgery performed was a unilateral thyroidectomy (10/14 dogs). Postoperatively, systemic therapy was administered in eight dogs. Six dogs developed local recurrence with a median time to loco‐regional recurrence of 53 days. Ten dogs developed metastatic disease with the most common metastatic site being the lungs (6/10 dogs), with a median time to metastasis of 93 days. Ten dogs were euthanised due to locoregional or distant progression of their mixed thyroid neoplasm. The overall median survival time was 156 days (95%CI: 49–244). The median survival time for dogs treated with adjuvant therapy was 189 days (95%CI: 24–244), whereas dogs without adjuvant therapy had a median survival time of 156 days (95%CI: 35‐upper limit could not be calculated; p = 0.62). Conclusion The thyroid tumours with both mesenchymal and epithelial components in this small sample set were associated with a poor prognosis after surgical excision with or without adjunctive therapy.

In recent years, there has been an increase in the usage of consumer based EEG devices with fewer channel configuration. Although independent component analysis has been a popular approach for eye-blink artifact removal from multichannel EEG signals, several studies showed that there is a leak of neural information into the eye-blink artifact associated independent components (ICs). Furthermore, the leak increases as the number of input EEG channels decreases and leads to loss of valuable EEG information. To overcome this problem, we developed a new framework that combines ICA with continuous wavelet transform (CWT), ${k}-$ means and singular spectrum analysis (SSA) methods. In contrast to the existing approaches, the artifact region in the identified eye-blink artifact IC is detected and suppressed rather than setting it to zero as in classical ICA. As most of the energy in the eye-blink artifact IC is concentrated in the artifact region, CWT and ${k}-$ means algorithms exploits this feature to detect the eye-blink artifact region. Support vector machine (SVM) based classifier is finally designed for automatic detection of the eye blink artifact ICs. The performance of proposed method is evaluated on synthetic and two real EEG datasets for various EEG channels setting. Results highlight that for fewer channel EEG signals, the proposed method provides accurate separation without any neural information loss as compared to the existing methods.

We studied 137 primary testicular germ cell tumors (TGCTs) using high-dimensional assays of genomic, epigenomic, transcriptomic, and proteomic features. These tumors exhibited high aneuploidy and a paucity of somatic mutations. Somatic mutation of only three genes achieved significance-KIT, KRAS, and NRAS-exclusively in samples with seminoma components. Integrated analyses identified distinct molecular patterns that characterized the major recognized histologic subtypes of TGCT: seminoma, embryonal carcinoma, yolk sac tumor, and teratoma. Striking differences in global DNA methylation and microRNA expression between histology subtypes highlight a likely role of epigenomic processes in determining histologic fates in TGCTs. We also identified a subset of pure seminomas defined by KIT mutations, increased immune infiltration, globally demethylated DNA, and decreased KRAS copy number. We report potential biomarkers for risk stratification, such as miRNA specifically expressed in teratoma, and others with molecular diagnostic potential, such as CpH (CpA/CpC/CpT) methylation identifying embryonal carcinomas.

Abstract In recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels or even with a single EEG channel. However, electrooculogram (EOG) signal, also known as eye-blink artifact, produced by involuntary movement of eyelids, always contaminate the EEG signals. Very few techniques are available to remove these artifacts from single channel EEG and most of these techniques modify the uncontaminated regions of the EEG signal. In this paper, we developed a new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal. The novelty of the work lies in the extraction of the eye-blink artifact based on the time-domain features of the EEG signal and the unsupervised machine learning algorithm. The extracted eye-blink artifact is further processed by the SSA method and finally subtracted from the contaminated single channel EEG signal to obtain the corrected EEG signal. Results with synthetic and real EEG signals demonstrate the superiority of the proposed method over the existing methods. Moreover, the frequency based measures [the power spectrum ratio ( $$\Gamma $$ Γ ) and the mean absolute error (MAE)] also show that the proposed method does not modify the uncontaminated regions of the EEG signal while removing the eye-blink artifact.

In this study, we investigate the effect of Clarke's subdifferential type on the optimal control results for fractional differential systems of order 1

Enterobacteriaceae pathogens such as Escherichia coli, Salmonella sp., Shigella sp., Proteus sp., and Klebsiella pneumoniae cause a wide range of gastrointestinal and other mucosal infections. These bacteria acquire antibiotic resistance very quickly and evolve into multi-drug resistant strains thereby making the treatment very difficult. The outer membrane proteins (OMPs) in Enterobacteriaceae are potential vaccine candidates owing for their high immunogenicity and amino acid conservation. The OmpA is one such protein which need to be investigated for the development of a potential subunit vaccine against multiple infections casued by the pathogens of Enterobacteriaceae. To investigate this, we expressed and purified the highly conserved OmpA of S. typhimurium and studied the antibody mediated cross reactivity with the other Enterobacteriaceae pathogens. This was validated through dot ELISA performed with the hyperimmune sera raised against rOmpA of S. typhimurium. We further analyzed the sequence of OmpA protein and clearly understood that the B-cell epitopes in the protein are highly conserved are responsible for cross reactivity among the Enterobacteriaceae pathogens. This work led to findings that provide strong evidence for the application of OmpA in broad-spectrum subunit vaccine against enteric infections. [ABSTRACT FROM AUTHOR]

Sorafenib is a multi-kinase small molecule inhibitor that targets serine/threonine and tyrosine kinases including the RAF kinase family, VEGFR-2, and PDGFR. The aim of this study was to evaluate the systemic pharmacokinetics of a previously defined tolerable oral dose of sorafenib in tumor-bearing dogs. Six client-owned dogs with a cytologic or histologic diagnosis of cancer were enrolled in this open-label, tolerability study. Dogs were administered sorafenib at an intended dose of 3 mg/kg and serum samples were obtained for analysis of sorafenib serum concentrations at 0, 1, 2, 6, 12, 24, 48, 72, 96, and 168 h post-drug administration. Median time to peak serum sorafenib concentration occurred at 4 h (range 2–12 h) resulting in an average serum concentration of 54.9 ± 33.5 ng/mL (118.2 ± 72.1 nM). Mean sorafenib levels declined by over 70% relative to peak serum concentrations by 24 h in all dogs, suggesting the value of at least twice daily administration. Doses of 3 mg/kg were well-tolerated and no patients in the study experienced adverse events that were attributable to sorafenib. Future trials in dogs with cancer are recommended at this dosing schedule to assess the effect of sorafenib administration on anti-tumor efficacy signals and relevant pharmacodynamic target modulation in vivo.