Network Modeling and Control of Dynamic Disease Pathways, Review and Perspectives.

Dynamic disease pathways are a combination of complex dynamical processes among bio-molecules in a cell that leads to diseases. Network modeling of disease pathways considers disease-related bio-molecules (e.g. DNA, RNA, transcription factors, enzymes, proteins, and metabolites) and their interaction (e.g. DNA methylation, histone modification, alternative splicing, and protein modification) to study disease progression and predict therapeutic responses. These bio-molecules and their interactions are the basic elements in the study of the misregulation in the disease-related gene expression that lead to abnormal cellular responses. Gene regulatory networks, cell signaling networks, and metabolic networks are the three major types of intracellular networks for the study of the cellular responses elicited from extracellular signals. The disease-related cellular responses can be prevented or regulated by designing control strategies to manipulate these extracellular or other intracellular signals. The paper reviews the regulatory mechanisms, the dynamic models, and the control strategies for each intracellular network. The applications, limitations and the prospective for modeling and control are also discussed.

Gorlin-Goltz Syndrome - A Rare Case Entity in Young Child.

Gorlin-Goltz syndrome (GGS) is an infrequent multisystemic disease with an autosomal dominant trait, which depicted presence of numerous basal cell carcinoma in conjunction with multiorgan abnormalities. This syndrome may be diagnosed early by a dentist by routine radiographic exams in the first decade of life, since the keratocystic odontogenic tumour are usually one of the first manifestations of the syndrome. This article includes a case report of the GGS with regard to its history, incidence, etiology, features, investigations, diagnostic criteria, keratocystic odontogenic tumour and treatment modalities.

Understanding the Application of Mechanical Dyssynchrony in Patients with Heart Failure Considered for CRT.

Over the past two decades of CRT use, the failure rate has remained around 30-35%, despite several updates in the guidelines based on the understanding from multiple trials. This review article summarizes the role of mechanical dyssynchrony in the selection of heart failure patients for cardiac resynchronization therapy. Understanding the application of mechanical dyssynchrony has also evolved during these past two decades. There is no role of lone mechanical dyssynchrony in the patient selection for CRT. However, mechanical dyssynchrony can complement the electrocardiogram and clinical criteria and improve patient selection by reducing the failure rate. An oversimplified approach to mechanical dyssynchrony assessment, such as just estimating time-to-peak delays between segments, should not be used. Instead, methods that can identify the underlying pathophysiology of HF and are representative of a substrate to CRT should be applied.

Comparative outcomes in patients with preexisting heart failure to those without heart failure after out-of-hospital cardiac arrest: A nationwide registry study.

BACKGROUND: The knowledge of prognosis following out-of-hospital cardiac arrest (OHCA) in patients with heart failure heart failure (HF) is sparse. The objective of this study was to compare the outcome after OHCA among patients with and without HF. METHODS: We studied 45,293 patients who were included for the Danish cardiac arrest registry between 2001 and 2014. Patients were stratified into two groups based on the presence of HF prior to cardiac arrest. The primary outcome was 30-day survival and secondary outcome was anoxic brain damage or permanent nursing home admission at 1-year among 30-day survivors. RESULTS: Among the final 28,955 patients included, 6675 (23%) patients had prior HF and 22,280 (77%) patients had no prior HF. At 30 days, 616 (9.2%) patients survived among the patients with HF and 1916 (8.6%) among the patients without HF. There was a significant interaction between atrial fibrillation (AF) and HF for primary outcome and therefore it was assessed separately between the two study groups stratified based on AF. Among patients without AF a significantly higher odds of 30-day survival were observed among patients with HF (OR 2.69, 95% CI 2.34-3.08, P < 0.001), but no difference was observed among the patients from two study groups with no AF. No significant difference in risk for secondary outcome was observed among the two study groups. In multivariable average treatment effect modeling, all the results largely remain unchanged. CONCLUSIONS: Outcome following OHCA among patients with and without HF is found to be similar in this large Danish OHCA registry.

A Secure Vitals Monitoring Point-of-Care Device.

Point-of-care (POC) devices continuously monitor vital signs and provide health suggestions to users. However, the devices are not affordable to everyone due to their cost. Here, we design a POC device that can continuously estimate vital signs using fewer sensors and lower costs. We do so by measuring photoplethysmogram signals and temperature and then estimating the heart rate, blood oxygen saturation, respiration rate, and blood pressure. For keeping the vital data secure, an auto-encoder and a convolutional neural network were also used for encryption and abnormality detection, respectively. Tests on the hardware showed the design accurately obtained users' vitals. The proposed design is expected to be generalized to obtain other vitals and fabricated at a low cost, making it affordable to all people.

Koopman-based Impulsive Model Predictive Control of BCG Immunotherapy.

Bacillus of Calmette and Guerin (BCG) is the most effective immunologic treatment for non-muscle-invasive bladder cancer by stimulating the immune response of patients. The therapeutic performance of BCG treatment is limited by the dosing scheme, which is difficult to design due to nonlinear dynamics and constraints in the pharmacodynamic model. Here we present a computational method that combines linearization, impulsive control, and constrained optimization to design optimal drug dosing. We do so by first adopting Koopman theory to map the nonlinear pharmacodynamic model into linear space. Then we use model predictive control to design drug dosing schemes based on the transformed linear model with impulsive drug instillation, constrained by drug concentration. With this pipeline, we find that the Koopman-based linear system has almost identical dynamic behaviors to the original model based on numerical simulations. Also, the designed drug doses stay within the constraints and cancerous cell proliferation is effectively suppressed by driving the uninfected tumor cell population to a descending reference trajectory. Robustness tests are performed to show the proposed controller is robust to a certain level of model uncertainty. The method is expected to be generalized to the design of other model-based drug dosing schemes because of its optimality, impulsivity, and linearity.

A Rare Case of Central Odontogenic Fibroma of the Mandible.

The benign tumor central odontogenic fibroma (COF) accounts for less than 1% of all the existing odontogenic tumors. The mandibular or maxillary cortical plate is seen to show asymptotic diversification. It has been characterized as a benign jaw neoplasm. Radiographically, it primarily manifests as a multilocular radiolucency. Histologically, it comprises fibroblasts and mature collagen fibers. The popular choice for the management of COFs is enucleation, followed by the extraction of associated teeth. COFs have maintained a track record of showing rare chances of recurrence following surgery. COF was detected in a 38-year-old female who had edema in the lower right front tooth region. The lesion was surgically removed, and a histopathological examination was performed. Many case reports of COF have been stated in the literature. This indicates that cases of COF are not a rare appearance.

Bionanotechnology and bioMEMS (BNM): state-of-the-art applications, opportunities, and challenges.

The development of micro- and nanotechnology for biomedical applications has defined the cutting edge of medical technology for over three decades, as advancements in fabrication technology developed originally in the semiconductor industry have been applied to solving ever-more complex problems in medicine and biology. These technologies are ideally suited to interfacing with life sciences, since they are on the scale lengths as cells (microns) and biomacromolecules (nanometers). In this paper, we review the state of the art in bionanotechnology and bioMEMS (collectively BNM), including developments and challenges in the areas of BNM, such as microfluidic organ-on-chip devices, oral drug delivery, emerging technologies for managing infectious diseases, 3D printed microfluidic devices, AC electrokinetics, flexible MEMS devices, implantable microdevices, paper-based microfluidic platforms for cellular analysis, and wearable sensors for point-of-care testing.

Nonlinear control designs and their application to cancer differentiation therapy.

We designed three new controllers: a sigmoid-based controller, a polynomial dynamic inversion-based controller, and a proportional-integral-derivative (PID) impulsive controller for cancer differentiation therapy. We compared these three controllers to existing control strategies to show the improvement in performance and compare their robustness. The sigmoid-based controller adds a sigmoid term associated with the error of the controlled state and a selected observed state. The sigmoid term is multiplied by a control gain, thereby decreasing the control effort for state transition. The polynomial dynamic inversion-based controller adds a cubic error term in the error dynamic aiming to achieve a shorter convergence time to the desired value of the controlled state. The PID impulsive controller considers the accumulated controlled state error and the rate of change of the controlled state error, thereby forcing the controlled state to converge to the desired value and alleviating the damping effect in the steady state. For the considered cancer network, the 3 new cancer control strategies exhibit superior and robust performance. The PID impulsive controller has a significant improvement in robustness compared to the impulsive controller and has greater potential for cancer differentiation therapy.

Reparameterized multiobjective control of BCG immunotherapy.

Bladder cancer is a cancerous disease that mainly affects elder men and women. The immunotherapy that uses Bacillus of Calmette and Guerin (BCG) effectively treats bladder cancer by stimulating the immune response of patients. The therapeutic performance of BCG relies on drug dosing, and the design of an optimal BCG regimen is an open question. In this study, we propose the reparameterized multiobjective control (RMC) approach for seeking an optimal drug dosing regimen and apply it to the design of BCG treatment. This approach utilizes constrained optimization based on a nonlinear bladder cancer model with impulsive drug instillation. We compare the performance of RMC with Koopman model predictive control (MPC) and validate the efficacy of optimal BCG dosing regimens through numerical simulations, demonstrating the efficient elimination of cancerous cells. The proposed control framework holds the potential for generalization to other model-based treatment designs.

Zn(II)-Coordination Complex(s) from Chiral Bisamides of L-Phenylalanine: Nanoscale-Based Biological Applications and Metallogelation.

Three 3-pyridyl-containing small organic bisamide molecules attached with innocent L-phenylalanine (PHE) side chain as building blocks and positional isomeric toluoyl terminals (PME, MME, and OME) have rationally been designed and synthesized for developing a new series of ZnII-coordination complexes. One of the unique molecular frameworks, having two hydrogen bond-equipped monodentate metal-coordinating sites and biologically potent chiral PHE moiety, was combined with ZnII halide salts under various conditions to produce the coordination complexes (CC1-CC7), thoroughly characterized by the single-crystal X-ray diffraction (SXRD) technique. Maintaining the similar component ratios of acquired CCs in 1:1 DMSO-water produced low-molecular weight metallogels (LMWGs) of PME/MME as envisaged from a rheology- and crystal engineering-based structural rationale. A structure-property correlation from the basis of PXRD of the bulk and xerogels and SXRD data of the isolated single crystals of reaction products clearly supports the crystal engineering-based design strategy based on which the metallogels are prepared. Hand-ground nanoscale ZnCl2-based coordination complex CC1 of PME was also studied for cytotoxicity (HEK-293 cell line) and anticancer activities (B16-F10 cell line) in the MTT assay.

Further developments of the extended quadrature method of moments to solve population balance equations.

Developing numerical methods to solve polydispersed flows using a Population Balance Equation (PBE) is an active research topic with wide engineering applications. The Extended Quadrature Method of Moments (EQMOM) approximates the number density as a positive mixture of Kernel Density Functions (KDFs) that allows physical source terms in the PBEs to compute continuous or point-wise form according to the moments. The moment-inversion procedure used in EQMOM has limitations such as the inability to calculate certain roots even if it is defined, absence of consistent result when multiple roots exist or when the roots are nearly equal. To address these limitations, the study proposes a modification of the moment-inversion procedure to solve the PBE based on the proposed Halley-Ridder (H-R) method. Although there is no significant improvement in the extent of variability relative to the mean of the tested shape parameter σ values, an increase in the number of floating point operations (FLOPS) is observed which the proposed algorithm responds in limitations mentioned above. The total number of FLOPS for all the kernels used for the approximation increased by around 30%. This is an improvement towards the development of a more reliable and robust moment-inversion procedure.

Neutrophils in Cancer and Potential Therapeutic Strategies Using Neutrophil-Derived Exosomes.

Neutrophils are the most abundant immune cells and make up about 70% of white blood cells in human blood and play a critical role as the first line of defense in the innate immune response. They also help regulate the inflammatory environment to promote tissue repair. However, in cancer, neutrophils can be manipulated by tumors to either promote or hinder tumor growth depending on the cytokine pool. Studies have shown that tumor-bearing mice have increased levels of neutrophils in peripheral circulation and that neutrophil-derived exosomes can deliver various cargos, including lncRNA and miRNA, which contribute to tumor growth and degradation of extracellular matrix. Exosomes derived from immune cells generally possess anti-tumor activities and induce tumor-cell apoptosis by delivering cytotoxic proteins, ROS generation, H2O2 or activation of Fas-mediated apoptosis in target cells. Engineered exosome-like nanovesicles have been developed to deliver chemotherapeutic drugs precisely to tumor cells. However, tumor-derived exosomes can aggravate cancer-associated thrombosis through the formation of neutrophil extracellular traps. Despite the advancements in neutrophil-related research, a detailed understanding of tumor-neutrophil crosstalk is still lacking and remains a major barrier in developing neutrophil-based or targeted therapy. This review will focus on the communication pathways between tumors and neutrophils, and the role of neutrophil-derived exosomes (NDEs) in tumor growth. Additionally, potential strategies to manipulate NDEs for therapeutic purposes will be discussed.

Assessment of health burden due to the emissions of fine particulate matter from motor vehicles: A case of Nakhon Ratchasima province, Thailand.

Air pollution, owing to the ever-increasing transport vehicle fleet, and adverse health effects are increasing in provinces of Thailand. The study estimated that the vehicle fleet size of Nakhon Ratchasima (NR) province of Thailand will grow to 2 million vehicles by 2030, which was 1.36 million in 2021. In NR, the PM2.5 and PM10 concentrations already surpassed both WHO and NAAQS guidelines in 2019-2021. Using Pollution Control Department (PCD) approved Tier I and II Methodology of EMEP/EEA, this research estimated that the total tailpipe emission load will be 1039 tons of PM2.5, 16,630 tons of NO2, 20,623 tons of CO, 195 tons NH3, and 249 tons of SO2 in NR during 2030. The emission load will increase to 1752 tons of PM2.5, 21,126 tons of NO2, 25,559 tons of CO, 361 tons of NH3 and 9344 tons of SO2 during 2030 if upstream emissions are considered. This study has developed five control scenarios in line with the directives of PCD to mitigate the adverse health from vehicle-led air pollution in NR and implementation during 2024-2030. According to the study, different control scenarios to be implemented during 2024-2030, will be able to keep the fleet size of vehicles in the NR under control. The results show that the control scenarios will keep the annual tailpipe emission of PM2.5 at 604 tons in 2030, a 42 % reduction over the 2030 Business-As-Usual scenario (BAU). The health damage in the range of 6941 to 11,625 disability-adjusted life years (DALYs) under the 2030 BAU scenario in NR due to tailpipe and upstream emissions can be reduced to 4162-7318 DALYs with the implementation of different control scenarios. The control scenarios will also provide significant economic benefits ranging from 4465 to 6718 million THB during 2024-2030 through reduced DALYs and associated costs.

Phenylalanine conjugated supramolecular hydrogels developed from the mafenide and flurbiprofen multidrug for biological applications.

A well-known nonsteroidal anti-inflammatory drug (NSAID), flurbiprofen (FLR), was first conjugated individually with two naturally occurring amino acids such as L-phenylalanine (PHE) and L-alanine (ALA). These covalent amidic bioconjugates were further reacted individually with mafenide (a drug for treating burn wounds) and amantadine (an antiviral drug) to develop primary ammonium monocarboxylate (PAM) salts. Interestingly, both the PHE-containing multidrug salts exhibited significant gelation ability with various solvents including biologically potent water or methyl salicylate (MS). The isolated hydrogel (HG) as well as all the organogels obtained from multidrug gelators were extensively characterized by dynamic rheology and rheoreversibility studies. The hydrogel of FLR·PHE·MAF and MS gels of FLR·PHE·AMN/FLR·AMN were also selectively characterized by table-top and FEG-TEM analyses. The temperature-dependent 1H-NMR spectroscopy of the selected HG further provided insights into the gelation mechanism and the only isolated single-crystal of the weakly diffracted gelator FLR·AMN also revealed the presence of 1D hydrogen-bonded networks. The pure hydrogelator FLR·PHE·MAF salt (which is also an ambidextrous gelator) was found to be promising in both mechanical (rheoreversible) and biological applications and was found to be effective in cytotoxicity, biocompatibility, anti-cancer activity (MTT and cell migration assay), antibacterial response (zone inhibition, turbidity, INT, and resazurin assay) and haemolysis studies.

Lessons from the meiotic recombination landscape of the ZMM deficient budding yeast Lachancea waltii.

Meiotic recombination is a driving force for genome evolution, deeply characterized in a few model species, notably in the budding yeast Saccharomyces cerevisiae. Interestingly, Zip2, Zip3, Zip4, Spo16, Msh4, and Msh5, members of the so-called ZMM pathway that implements the interfering meiotic crossover pathway in S. cerevisiae, have been lost in Lachancea yeast species after the divergence of Lachancea kluyveri from the rest of the clade. In this context, after investigating meiosis in L. kluyveri, we determined the meiotic recombination landscape of Lachancea waltii. Attempts to generate diploid strains with fully hybrid genomes invariably resulted in strains with frequent whole-chromosome aneuploidy and multiple extended regions of loss of heterozygosity (LOH), which mechanistic origin is so far unclear. Despite the lack of multiple ZMM pro-crossover factors in L. waltii, numbers of crossovers and noncrossovers per meiosis were higher than in L. kluyveri but lower than in S. cerevisiae, for comparable genome sizes. Similar to L. kluyveri but opposite to S. cerevisiae, L. waltii exhibits an elevated frequency of zero-crossover bivalents. Lengths of gene conversion tracts for both crossovers and non-crossovers in L. waltii were comparable to those observed in S. cerevisiae and shorter than in L. kluyveri despite the lack of Mlh2, a factor limiting conversion tract size in S. cerevisiae. L. waltii recombination hotspots were not shared with either S. cerevisiae or L. kluyveri, showing that meiotic recombination hotspots can evolve at a rather limited evolutionary scale within budding yeasts. Finally, L. waltii crossover interference was reduced relative to S. cerevisiae, with interference being detected only in the 25 kb distance range. Detection of positive inference only at short distance scales in the absence of multiple ZMM factors required for interference-sensitive crossovers in other systems likely reflects interference between early recombination precursors such as DSBs.

Computational systems biology in disease modeling and control, review and perspectives.

Omics-based approaches have become increasingly influential in identifying disease mechanisms and drug responses. Considering that diseases and drug responses are co-expressed and regulated in the relevant omics data interactions, the traditional way of grabbing omics data from single isolated layers cannot always obtain valuable inference. Also, drugs have adverse effects that may impair patients, and launching new medicines for diseases is costly. To resolve the above difficulties, systems biology is applied to predict potential molecular interactions by integrating omics data from genomic, proteomic, transcriptional, and metabolic layers. Combined with known drug reactions, the resulting models improve medicines' therapeutical performance by re-purposing the existing drugs and combining drug molecules without off-target effects. Based on the identified computational models, drug administration control laws are designed to balance toxicity and efficacy. This review introduces biomedical applications and analyses of interactions among gene, protein and drug molecules for modeling disease mechanisms and drug responses. The therapeutical performance can be improved by combining the predictive and computational models with drug administration designed by control laws. The challenges are also discussed for its clinical uses in this work.

Loss of Heterozygosity Spectrum Depends on Ploidy Level in Natural Yeast Populations.

The appearance of genomic variations such as loss of heterozygosity (LOH) has a significant impact on phenotypic diversity observed in a population. Recent large-scale yeast population genomic surveys have shown a high frequency of these events in natural isolates and more particularly in polyploids. However, the frequency, extent, and spectrum of LOH in polyploid organisms have never been explored and are poorly characterized to date. Here, we accumulated 5,163 LOH events over 1,875 generations in 76 mutation accumulation (MA) lines comprising nine natural heterozygous diploid, triploid, and tetraploid natural S. cerevisiae isolates from different ecological and geographical origins. We found that the rate and spectrum of LOH are variable across ploidy levels. Of the total accumulated LOH events, 8.5%, 21%, and 70.5% of them were found in diploid, triploid, and tetraploid MA lines, respectively. Our results clearly show that the frequency of generated LOH events increases with ploidy level. In fact, the cumulative LOH rates were estimated to be 9.3 × 10-3, 2.2 × 10-2, and 8.4 × 10-2 events per division for diploids, triploids, and tetraploids, respectively. In addition, a clear bias toward the accumulation of interstitial and short LOH tracts is observed in triploids and tetraploids compared with diploids. The variation of the frequency and spectrum of LOH events across ploidy level could be related to the genomic instability, characterizing higher ploidy isolates.

Air pollution in Delhi, India: It's status and association with respiratory diseases.

The policymakers need research studies indicating the role of different pollutants with morbidity for polluted cities to install a strategic air quality management system. This study critically assessed the air pollution of Delhi for 2016-18 to found out the role of air pollutants in respiratory morbidity under the ICD-10, J00-J99. The critical assessment of Delhi air pollution was done using various approaches. The mean PM2.5 and PM10 concentrations during the measurement period exceeded both national and international standards by a wide margin. Time series charts indicated the interdependence of PM2.5 and PM10 and connection with hospital visits due to respiratory diseases. Violin plots showed that daily respiratory disease hospital visits increased during the winter and autumn seasons. The winter season was the worst from the city's air pollution point of view, as revealed by frequency analyses. The single and multi-pollutant GAM models indicated that short-term exposure to PM10 and SO2 led to increased hospital visits due to respiratory diseases. Per 10 units increase in concentrations of PM10 brought the highest increase in hospital visits of 0.21% (RR: 1.00, 95% CI: 1.001, 1.002) at lag0-6 days. This study found the robust effect of SO2 persisted in Delhi from lag0 to lag4 days and lag01 to lag06 days for single and cumulative lag day effects, respectively. While every 10 µg m-3 increase of SO2 concentrations on the same day (lag0) led to 32.59% (RR: 1.33, 95% CI: 1.09, 1.61) rise of hospital visits, the cumulative concentration of lag0-1 led to 37.21% (RR: 1.37, 95% CI:1.11, 1.70) rise in hospital visits which further increased to even 83.33% (RR: 1.83, 95% CI:1.35, 2.49) rise at a lag0-6 cumulative concentration in Delhi. The role of SO2 in inducing respiratory diseases is worrying as India is now the largest anthropogenic SO2 emitter in the world.

Predicting Drug Mechanics by Deep Learning on Gene and Cell Activities.

The identification of protein target and mechanism of disease are fundamentally important in drug discovery. A pipeline for predicting mechanism of action (MoA) for drug molecules based on gene expression and cell viability data is developed and demonstrated on experimental data. A deep learning network learns the known MoAs over thousands of gene expression and cell viability training data and is shown to predict the unknown MoAs over test data with high efficacy.