Notes

The expansion and also preliminary evaluation of referral flowchart with regard to thought neuroblastoma with regard to pediatricians inside nononcology clinics inside Cina.
The first 1000 days from conception to two-years of age are a critical period in brain development, and there is an increasing drive for developing technologies to help advance our understanding of neurodevelopmental processes during this time. Functional near-infrared spectroscopy (fNIRS) has enabled longitudinal infant brain function to be studied in a multitude of settings. Conventional fNIRS analyses tend to occur in the channel-space, where data from equivalent channels across individuals are combined, which implicitly assumes that head size and source-detector positions (i.e. array position) on the scalp are constant across individuals. The validity of such assumptions in longitudinal infant fNIRS analyses, where head growth is most rapid, has not previously been investigated. We employed an image reconstruction approach to analyse fNIRS data collected from a longitudinal cohort of infants in The Gambia aged 5- to 12-months. This enabled us to investigate the effect of variability in both head size and array position on the anatomical and statistical inferences drawn from the data at both the group- and the individual-level. We also sought to investigate the impact of group size on inferences drawn from the data. We found that variability in array position was the driving factor between differing inferences drawn from the data at both the individual- and group-level, but its effect was weakened as group size increased towards the full cohort size (N = 53 at 5-months, N = 40 at 8-months and N = 45 at 12-months). We conclude that, at the group sizes in our dataset, group-level channel-space analysis of longitudinal infant fNIRS data is robust to assumptions about head size and array position given the variability in these parameters in our dataset. These findings support a more widespread use of image reconstruction techniques in longitudinal infant fNIRS studies.PnPP-19 peptide has a primary sequence design based on molecular modeling studies of PnTx2-6 toxin. It comprises the amino acid residues that are potentially significant for the pharmacological action of PnTx2-6. Ex vivo and in vivo experiments in normotensive, hypertensive, or diabetic murine models have shown a significant improvement in penile erection after administration of PnPP-19. Given the potential use of PnPP-19 in pharmaceutical formulations to treat erectile dysfunction and the lack of information concerning its mode of action, the present work investigates its activities on the nitrergic system. PnPP-19 induced a significant increase in nitric oxide (NO) and cGMP levels in corpus cavernosum (cc). These effects were inhibited by l-NAME, a non-selective inhibitor of nitric oxide synthase (NOS); were partially inhibited by 7- Nitroindazole, a selective inhibitor of neuronal NOS (nNOS); and were abolished by L-NIL, a selective inhibitor of inducible NOS (iNOS). This potentiating effect was not affected by atropine. PnPP-19 also led to changes in mRNA levels, protein expression and phosphorylation at specific sites of NOS, in cc. Assays using cavernous tissue from knockout mice to endothelial NOS (eNOS), nNOS or iNOS showed that PnPP-19 potentiates relaxation only in eNOS-knockout mice, which suggests an essential role for nNOS. Surprisingly, iNOS enhanced the potentiation of erectile function evoked by PnPP-19. Our results demonstrate that this new synthetic peptide potentiates erectile function via nitric oxide activation and reinforce its role as a new pharmacological tool for the treatment of erectile dysfunction.Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning is an essential alternative for developing new drugs for a disease subpopulation. This shows the importance of developing data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI approach for patient stratification and drug repositioning. Contrast pattern mining and network analysis were used to discover homogeneous subgroups within a disease population. For each subgroup, a biomedical network analysis was done to find the drugs that are most relevant to a given subgroup of patients. The set of candidate drugs for each subgroup was ranked using an aggregated drug score assigned to each drug. The proposed method represents a human-in-the-loop framework, where medical experts use the data-driven results to generate hypotheses and obtain insights into potential therapeutic candidates for patients who belong to a subgroup. Colorectal cancer (CRC) was used as a case study. Patients' phenotypic and genotypic data was utilized with a heterogeneous knowledge base because it gives a multi-view perspective for finding new indications for drugs outside of their original use. Our analysis of the top candidate drugs for the subgroups identified by medical experts showed that most of these drugs are cancer-related, and most of them have the potential to be a CRC regimen based on studies in the literature.Within the recent pandemic, scientists and clinicians are engaged in seeking new technology to stop or slow down the COVID-19 pandemic. The benefit of machine learning, as an essential aspect of artificial intelligence, on past epidemics offers a new line to tackle the novel Coronavirus outbreak. Accurate short-term forecasting of COVID-19 spread plays an essential role in improving the management of the overcrowding problem in hospitals and enables appropriate optimization of the available resources (i.e., materials and staff).This paper presents a comparative study of machine learning methods for COVID-19 transmission forecasting. We investigated the performances of deep learning methods, including the hybrid convolutional neural networks-Long short-term memory (LSTM-CNN), the hybrid gated recurrent unit-convolutional neural networks (GAN-GRU), GAN, CNN, LSTM, and Restricted Boltzmann Machine (RBM), as well as baseline machine learning methods, namely logistic regression (LR) and support vector regression (SVR). The employment of hybrid models (i.e., LSTM-CNN and GAN-GRU) is expected to eventually improve the forecasting accuracy of COVID-19 future trends. The performance of the investigated deep learning and machine learning models was tested using confirmed and recovered COVID-19 cases time-series data from seven impacted countries Brazil, France, India, Mexico, Russia, Saudi Arabia, and the US. The results reveal that hybrid deep learning models can efficiently forecast COVID-19 cases. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models. Furthermore, results showed that LSTM-CNN achieved improved performances with an averaged mean absolute percentage error of 3.718%, among others.The Fundulus genus of killifish includes species that inhabit marshes along the U.S. Atlantic coast and the Gulf of Mexico, but differ in their ability to adjust rapidly to fluctuations in salinity. Previous work suggests that euryhaline killifish stimulate polyamine biosynthesis and accumulate putrescine in the gills during acute hypoosmotic challenge. Despite evidence that polyamines have an osmoregulatory role in euryhaline killifish species, their function in marine species is unknown. Furthermore, the consequences of hypoosmotic-induced changes in polyamine synthesis on downstream pathways, such as ƴ-aminobutyric acid (Gaba) production, have yet to be explored. Here, we examined the effects of acute hypoosmotic exposure on polyamine, glutamate, and Gaba levels in the gills of a marine (F. majalis) and two euryhaline killifish species (F. heteroclitus and F. grandis). Fish acclimated to 32 ppt or 12 ppt water were transferred to fresh water, and concentrations of glutamate (Glu), Gaba, and the polyamines putrescine (Put), spermidine (Spd), and spermine (Spm) were measured in the gills using high-performance liquid chromatography. F. heteroclitus and F. grandis exhibited an increase in gill Put concentration, but showed no change in Glu or Gaba levels following freshwater transfer. F. heteroclitus also accumulated Spd in the gills, whereas F. grandis showed transient increases in Spd and Spm levels. In contrast, gill Put, Spm, Glu, and Gaba levels decreased in F. majalis following freshwater transfer. Together, these findings suggest that increasing polyamine levels and maintaining Glu and Gaba levels in the gills may enable euryhaline teleosts to acclimate to shifts in environmental salinity.Although the influence of the series elastic element of the muscle-tendon unit on jump performance has been investigated, the corresponding effect of the parallel elastic element remains unclear. This study examined the relationship between the resting calf muscle stiffness and drop jump performance. Twenty-four healthy men participated in this study. The shear moduli of the medial gastrocnemius and the soleus were measured at rest as an index of muscle stiffness using ultrasound shear wave elastography. The participants performed drop jumps from a 15 cm high box. The Spearman rank correlation coefficient was used to examine the relationships between shear moduli of the muscles and drop jump performance. The medial gastrocnemius shear modulus showed a significant correlation with the drop jump index (jump height/contact time) (r = 0.414, P = 0.044) and jump height (r = 0.411, P = 0.046), but not with contact time (P > 0.05). The soleus shear modulus did not correlate with these jump parameters (P > 0.05). These results suggest that the resting medial gastrocnemius stiffness can be considered as one of the factors that influence drop jump performance. Therefore, increase in resting muscle stiffness should enhance explosive athletic performance in training regimens.Neglected tropical diseases (NTDs) are parasitic and bacterial infections that are widespread, especially in the tropics, and cause health problems for about one billion people over 149 countries worldwide. However, in terms of therapeutic agents, for example, nifurtimox and benznidazole were developed in the 1960s to treat Chagas disease, but new drugs are desirable because of their side effects. Drug discovery takes 12 to 14 years and costs $2.6 billon dollars, and hence, computer aided drug discovery (CADD) technology is expected to reduce the time and cost. This paper describes our methods and results based on CADD, mainly for NTDs. An overview of databases, molecular simulation and pharmacophore modeling, contest-based drug discovery, and machine learning and their results are presented herein.Apicomplexa mainly comprises parasitic species and some of them, which infect and cause severe diseases to humans and livestock, have been extensively studied due to the clinical and industrial importance. Besides, apicomplexans are a popular subject of the studies focusing on the evolution initiated by a secondary loss of photosynthesis. By interpreting the position in the tree of eukaryotes and lifestyles of the phylogenetic relatives parsimoniously, the extant apicomplexans are predicted to be the descendants of a parasite bearing a non-photosynthetic (cryptic) plastid. The plastid-bearing characteristic for the ancestral apicomplexan is further strengthened by non-photosynthetic plastids found in the extant apicomplexans. The research on apicomplexan members infecting invertebrates is much less advanced than that on the pathogens to humans and livestock. Gregarines are apicomplexans that infect diverse invertebrates and recent studies based on transcriptome data revealed the presence of cryptic plastids in a subset of the species investigated.
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