Developing a forecasting model for cholera incidence in Dhaka megacity through time series climate data.

Cholera, an acute diarrheal disease spread by lack of hygiene and contaminated water, is a major public health risk in many countries. As cholera is triggered by environmental conditions influenced by climatic variables, establishing a correlation between cholera incidence and climatic variables would provide an opportunity to develop a cholera forecasting model. Considering the auto-regressive nature and the seasonal behavioral patterns of cholera, a seasonal-auto-regressive-integrated-moving-average (SARIMA) model was used for time-series analysis during 2000-2013. As both rainfall (r = 0.43) and maximum temperature (r = 0.56) have the strongest influence on the occurrence of cholera incidence, single-variable (SVMs) and multi-variable SARIMA models (MVMs) were developed, compared and tested for evaluating their relationship with cholera incidence. A low relationship was found with relative humidity (r = 0.28), ENSO (r = 0.21) and SOI (r = -0.23). Using SVM for a 1 °C increase in maximum temperature at one-month lead time showed a 7% increase of cholera incidence (p < 0.001). However, MVM (AIC = 15, BIC = 36) showed better performance than SVM (AIC = 21, BIC = 39). An MVM using rainfall and monthly mean daily maximum temperature with a one-month lead time showed a better fit (RMSE = 14.7, MAE = 11) than the MVM with no lead time (RMSE = 16.2, MAE = 13.2) in forecasting. This result will assist in predicting cholera risks and better preparedness for public health management in the future

Visualization of Glutamine Transporter Activities in Living Cells Using Genetically Encoded Glutamine Sensors

Glutamine plays a central role in the metabolism of critical biological molecules such as amino acids, proteins, neurotransmitters, and glutathione. Since glutamine metabolism is regulated through multiple enzymes and transporters, the cellular glutamine concentration is expected to be temporally dynamic. Moreover, differentiation in glutamine metabolism between cell types in the same tissue (e.g. neuronal and glial cells) is often crucial for the proper function of the tissue as a whole, yet assessing cell-type specific activities of transporters and enzymes in such heterogenic tissue by physical fractionation is extremely challenging. Therefore, a method of reporting glutamine dynamics at the cellular level is highly desirable. Genetically encoded sensors can be targeted to a specific cell type, hence addressing this knowledge gap. Here we report the development of Föster Resonance Energy Transfer (FRET) glutamine sensors based on improved cyan and yellow fluorescent proteins, monomeric Teal Fluorescent Protein (mTFP)1 and venus. These sensors were found to be specific to glutamine, and stable to pH-changes within a physiological range. Using cos7 cells expressing the human glutamine transporter ASCT2 as a model, we demonstrate that the properties of the glutamine transporter can easily be analyzed with these sensors. The range of glutamine concentration change in a given cell can also be estimated using sensors with different affinities. Moreover, the mTFP1-venus FRET pair can be duplexed with another FRET pair, mAmetrine and tdTomato, opening up the possibility for real-time imaging of another molecule. These novel glutamine sensors will be useful tools to analyze specificities of glutamine metabolism at the single-cell level

A model for homeopathic remedy effects: low dose nanoparticles, allostatic cross-adaptation, and time-dependent sensitization in a complex adaptive system

BACKGROUND: This paper proposes a novel model for homeopathic remedy action on living systems. Research indicates that homeopathic remedies (a) contain measurable source and silica nanoparticles heterogeneously dispersed in colloidal solution; (b) act by modulating biological function of the allostatic stress response network (c) evoke biphasic actions on living systems via organism-dependent adaptive and endogenously amplified effects; (d) improve systemic resilience. DISCUSSION: The proposed active components of homeopathic remedies are nanoparticles of source substance in water-based colloidal solution, not bulk-form drugs. Nanoparticles have unique biological and physico-chemical properties, including increased catalytic reactivity, protein and DNA adsorption, bioavailability, dose-sparing, electromagnetic, and quantum effects different from bulk-form materials. Trituration and/or liquid succussions during classical remedy preparation create “top-down” nanostructures. Plants can biosynthesize remedy-templated silica nanostructures. Nanoparticles stimulate hormesis, a beneficial low-dose adaptive response. Homeopathic remedies prescribed in low doses spaced intermittently over time act as biological signals that stimulate the organism’s allostatic biological stress response network, evoking nonlinear modulatory, self-organizing change. Potential mechanisms include time-dependent sensitization (TDS), a type of adaptive plasticity/metaplasticity involving progressive amplification of host responses, which reverse direction and oscillate at physiological limits. To mobilize hormesis and TDS, the remedy must be appraised as a salient, but low level, novel threat, stressor, or homeostatic disruption for the whole organism. Silica nanoparticles adsorb remedy source and amplify effects. Properly-timed remedy dosing elicits disease-primed compensatory reversal in direction of maladaptive dynamics of the allostatic network, thus promoting resilience and recovery from disease. SUMMARY: Homeopathic remedies are proposed as source nanoparticles that mobilize hormesis and time-dependent sensitization via non-pharmacological effects on specific biological adaptive and amplification mechanisms. The nanoparticle nature of remedies would distinguish them from conventional bulk drugs in structure, morphology, and functional properties. Outcomes would depend upon the ability of the organism to respond to the remedy as a novel stressor or heterotypic biological threat, initiating reversals of cumulative, cross-adapted biological maladaptations underlying disease in the allostatic stress response network. Systemic resilience would improve. This model provides a foundation for theory-driven research on the role of nanomaterials in living systems, mechanisms of homeopathic remedy actions and translational uses in nanomedicine

AI blood signature in common blood tests for detection of gastric cancer in a cohort of 190,000 individuals

Background: Gastric cancer is the fourth leading cause of cancer death worldwide. Lack of symptoms in the early stage of disease leads to delayed presentation and low overall survival rate. Early detection is key, but there is not yet an established test for non-invasive gastric cancer screening. Routine blood test panels, including complete blood count, liver function, renal function and clotting profiles, could be potentially useful in reflecting bodily processes related to cancer. Current literature and territory-wide statistical data analyses from Hong Kong have shown these subtle changes and differences in measured levels of subcomponents between individuals with and without gastric cancer, that are not recognized clinically, form recognizable patterns distinct to gastric cancer. In this study, we hypothesized that gastric cancer signatures could be identified in routine blood data. Deep learning AI algorithms are used to identify the gastric cancer signature. Results from testing and validation of the identified routine blood signature using a Big Clinical Data cohort are reported. Methods: This is a territory-wide healthcare database study using data from Hong Kong Hospital Authority data collaboration laboratory. All patients prescribed with medications for dyspepsia and with all blood tests components (CBC, LFT, RFT, clotting function) available in the period between 2004-2015 inclusive were included. In the period, data from 2004 to 09 and 2011 to 14 were used as training cohort and the rest were used as testing cohort. A blood gastric cancer signature was generated using deep learning AI algorithms, and applied to predict gastric cancer in Big Data population. Results: 193,117 patients were included in the captioned period, in whom 4,790 patients were diagnosed to have gastric cancer. 151,449 (3,815 pts with gastric cancer and 147,634 pts disease free) patients were included In training cohort. In the testing cohort in 2010 and 2015 (975 pts with gastric cancer and 40,693 pts disease-free), the tumor signature yielded a sensitivity of 0.79, specificity of 0.99, positive predictive value of 0.95 and a negative predictive value of 0.99. Conclusions: The hypothesis of the presence of a distinctive gastric cancer signature from parameters in routine blood test data is tested and validated in a large population cohort with dyspepsia. The signature has high accuracy and high sensitivity with low false positive and false negative rates. Multicenter prospective testing is being conducted in cohort indicated for OGD in Hong Kong

NLRP3 is activated in Alzheimer's disease and contributes to pathology in APP/PS1 mice.

Alzheimer's disease is the world's most common dementing illness. Deposition of amyloid-β peptide drives cerebral neuroinflammation by activating microglia. Indeed, amyloid-β activation of the NLRP3 inflammasome in microglia is fundamental for interleukin-1β maturation and subsequent inflammatory events. However, it remains unknown whether NLRP3 activation contributes to Alzheimer's disease in vivo. Here we demonstrate strongly enhanced active caspase-1 expression in human mild cognitive impairment and brains with Alzheimer's disease, suggesting a role for the inflammasome in this neurodegenerative disease. Nlrp3(-/-) or Casp1(-/-) mice carrying mutations associated with familial Alzheimer's disease were largely protected from loss of spatial memory and other sequelae associated with Alzheimer's disease, and demonstrated reduced brain caspase-1 and interleukin-1β activation as well as enhanced amyloid-β clearance. Furthermore, NLRP3 inflammasome deficiency skewed microglial cells to an M2 phenotype and resulted in the decreased deposition of amyloid-β in the APP/PS1 model of Alzheimer's disease. These results show an important role for the NLRP3/caspase-1 axis in the pathogenesis of Alzheimer's disease, and suggest that NLRP3 inflammasome inhibition represents a new therapeutic intervention for the disease