Binding and selectivity studies of phosphatidylinositol 3-kinase (PI3K) inhibitors.

Overexpression of the Phosphatidylinositol 3-kinase (PI3K) proteins have been observed in cancer cells. Targeting the phosphatidylinositol 3-kinase (PI3K) signaling transduction pathway by inhibition of the PI3K substrate recognition sites has been proved to be an effective approach to block cancer progression. Many PI3K inhibitors have been developed. Seven drugs have been approved by the US FDA with a mechanism of targeting the phosphatidylinositol 3-kinase/protein kinase-B/mammalian target of rapamycin (PI3K/AKT/mTOR) signaling pathway. In this study, we used docking tools to investigate selective binding of ligands toward four different subtypes of PI3Ks (PI3Kα, PI3Kß, PI3Kγ and PI3Kδ). The affinity predicted from both the Glide dock and the Movable-Type (MT)-based free energy calculations agreed well with the experimental data. The validation of our predicted methods with a large dataset of 147 ligands showed very small mean errors. We identified residues that may dictate the subtype-specific binding. Particularly, residues Asp964, Ser806, Lys890 and Thr886 of PI3Kγ might be utilized for PI3Kγ-selective inhibitor design. Residues Val828, Trp760, Glu826 and Tyr813 may be important for PI3Kδ-selective inhibitor binding.

Profiling Immune Cells in the Kidney Using Tissue Cytometry and Machine Learning.

The immune system governs key functions that maintain renal homeostasis through various effector cells that reside in or infiltrate the kidney. These immune cells play an important role in shaping adaptive or maladaptive responses to local or systemic stress and injury. We increasingly recognize that microenvironments within the kidney are characterized by a unique distribution of immune cells, the function of which depends on this unique spatial localization. Therefore, quantitative profiling of immune cells in intact kidney tissue becomes essential, particularly at a scale and resolution that allow the detection of differences between the various "nephro-ecosystems" in health and disease. In this review, we discuss advancements in tissue cytometry of the kidney, performed through multiplexed confocal imaging and analysis using the Volumetric Tissue Exploration and Analysis (VTEA) software. We highlight how this tool has improved our understanding of the role of the immune system in the kidney and its relevance in the pathobiology of renal disease. We also discuss how the field is increasingly incorporating machine learning to enhance the analytic potential of imaging data and provide unbiased methods to explore and visualize multidimensional data. Such novel analytic methods could be particularly relevant when applied to profiling immune cells. Furthermore, machine-learning approaches applied to cytometry could present venues for nonexhaustive exploration and classification of cells from existing data and improving tissue economy. Therefore, tissue cytometry is transforming what used to be a qualitative assessment of the kidney into a highly quantitative, imaging-based "omics" assessment that complements other advanced molecular interrogation technologies.

Health problems among Forcibly Displaced Myanmar Nationals (FDMNs) admitted to the Medicine ward of Cox's Bazar Medical College Hospital.

Background: Forcibly Displaced Myanmar Nationals (FDMNs) or Rohingya refugees are one of the vulnerable groups suffering from different kinds of health problems but have been less reported yet. Therefore, the study was designed to delineate the health problems among FDMNs admitted to Cox's Bazar Medical College Hospital. Methods: This hospital-based cross-sectional study was conducted at the Medicine ward, Cox's Bazar Medical College Hospital, for a six-month period following approval. Rohingya refugees who were admitted during the study period were approached for inclusion. Informed written consent was ensured prior to participation. A structured questionnaire was used during data collection. Collected information was recorded in case record form. A total of 290 subjects were interviewed. Analysis was performed using the statistical package for social science (SPSS) version 20. Results: The mean age of the participants was 48.76 ± 18.67 years (range: 16-91), with a clear male predominance (60.7%). Family size ranged 6-8. All of the participants reported at least one of the illnesses. Of all, 29.66% patients had disease of the respiratory system, and 26.9% had disease of the gastrointestinal and hepatobiliary system. Accidental injury or injury due to electrocution or thin falls or snake bites was present in 10.4% of the cases. Among the single most common diseases, COPD (20%) was the most frequently observed, and the rest of them were chronic liver disease (13.1%), pulmonary TB (5.5%), ischemic stroke (5.5%), CAP (4.1%), acute coronary syndrome (3.4%), thalassaemia (3.4%) and hepatocellular carcinoma (3.4%). Among the top 6 diagnosed diseases, PTB was more common in elderly individuals (p = 0.29). The disease pattern was similar across the sexes among the refugees except community acquisition pneumonia (CAP), which was commonly observed among males (p = .004). Considering different age groups, genitourinary problems were more common in males aged >60 years, and rheumatology and musculoskeletal problems were equally affected in females aged between 40 and 60 years. Conclusion: COPD, CLD and CAP were the most prevalent diseases in FDMN patients who attended the Medicine ward of Cox's Bazar Medical College Hospital. Further exploration is warranted before any policy making and comprehensive plan.

Informative Causality Extraction from Medical Literature via Dependency-Tree-Based Patterns.

Extracting cause-effect entities from medical literature is an important task in medical information retrieval. A solution for solving this task can be used for compilation of various causality relations, such as causality between disease and symptoms, between medications and side effects, and between genes and diseases. Existing solutions for extracting cause-effect entities work well for sentences where the cause and the effect phrases are name entities, single-word nouns, or noun phrases consisting of two to three words. Unfortunately, in medical literature, cause and effect phrases in a sentence are not simply nouns or noun phrases, rather they are complex phrases consisting of several words, and existing methods fail to correctly extract the cause and effect entities in such sentences. Partial extraction of cause and effect entities conveys poor quality, non-informative, and often, contradictory facts, comparing to the one intended in the given sentence. In this work, we solve this problem by designing an unsupervised method for cause and effect phrase extraction, PatternCausality, which is specifically suitable for the medical literature. Our proposed approach first uses a collection of cause-effect dependency patterns as template to extract head words of cause and effect phrases and then it uses a novel phrase extraction method to obtain complete and meaningful cause and effect phrases from a sentence. Experiments on a cause-effect dataset built from sentences from PubMed articles show that for extracting cause and effect entities, PatternCausality is substantially better than the existing methods-with an order of magnitude improvement in the F-score metric over the best of the existing methods. We also build different variants of PatternCausality, which use different phrase extraction methods; all variants are better than the existing methods. PatternCausality and its variants also show modest performance improvement over the existing methods for extracting cause and effect entities in a domain-neutral benchmark dataset, in which cause and effect entities are nouns or noun phrases consisting of one to two words.

Tissue Cytometry With Machine Learning in Kidney: From Small Specimens to Big Data.

Advances in cellular and molecular interrogation of kidney tissue have ushered a new era of understanding the pathogenesis of kidney disease and potentially identifying molecular targets for therapeutic intervention. Classifying cells in situ and identifying subtypes and states induced by injury is a foundational task in this context. High resolution Imaging-based approaches such as large-scale fluorescence 3D imaging offer significant advantages because they allow preservation of tissue architecture and provide a definition of the spatial context of each cell. We recently described the Volumetric Tissue Exploration and Analysis cytometry tool which enables an interactive analysis, quantitation and semiautomated classification of labeled cells in 3D image volumes. We also established and demonstrated an imaging-based classification using deep learning of cells in intact tissue using 3D nuclear staining with 4',6-diamidino-2-phenylindole (DAPI). In this mini-review, we will discuss recent advancements in analyzing 3D imaging of kidney tissue, and how combining machine learning with cytometry is a powerful approach to leverage the depth of content provided by high resolution imaging into a highly informative analytical output. Therefore, imaging a small tissue specimen will yield big scale data that will enable cell classification in a spatial context and provide novel insights on pathological changes induced by kidney disease.

Improving the Robustness of Object Detection Through a Multi-Camera-Based Fusion Algorithm Using Fuzzy Logic.

A single camera creates a bounding box (BB) for the detected object with certain accuracy through a convolutional neural network (CNN). However, a single RGB camera may not be able to capture the actual object within the BB even if the CNN detector accuracy is high for the object. In this research, we present a solution to this limitation through the usage of multiple cameras, projective transformation, and a fuzzy logic-based fusion. The proposed algorithm generates a "confidence score" for each frame to check the trustworthiness of the BB generated by the CNN detector. As a first step toward this solution, we created a two-camera setup to detect objects. Agricultural weed is used as objects to be detected. A CNN detector generates BB for each camera when weed is present. Then a projective transformation is used to project one camera's image plane to another camera's image plane. The intersect over union (IOU) overlap of the BB is computed when objects are detected correctly. Four different scenarios are generated based on how far the object is from the multi-camera setup, and IOU overlap is calculated for each scenario (ground truth). When objects are detected correctly and bounding boxes are at correct distance, the IOU overlap value should be close to the ground truth IOU overlap value. On the other hand, the IOU overlap value should differ if BBs are at incorrect positions. Mamdani fuzzy rules are generated using this reasoning, and three different confidence scores ("high," "ok," and "low") are given to each frame based on accuracy and position of BBs. The proposed algorithm was then tested under different conditions to check its validity. The confidence score of the proposed fuzzy system for three different scenarios supports the hypothesis that the multi-camera-based fusion algorithm improved the overall robustness of the detection system.

Type III Collagen is Required for Adipogenesis and Actin Stress Fibre Formation in 3T3-L1 Preadipocytes.

GPR56 is required for the adipogenesis of preadipocytes, and the role of one of its ligands, type III collagen (ColIII), was investigated here. ColIII expression was examined by reverse transcription quantitative polymerase chain reaction, immunoblotting and immunostaining, and its function investigated by knockdown and genome editing in 3T3-L1 cells. Adipogenesis was assessed by oil red O staining of neutral cell lipids and production of established marker and regulator proteins. siRNA-mediated knockdown significantly reduced Col3a1 transcripts, ColIII protein and lipid accumulation in 3T3-L1 differentiating cells. Col3a1-/- 3T3-L1 genome-edited cell lines abolished adipogenesis, demonstrated by a dramatic reduction in adipogenic moderators: Pparγ2 (88%) and C/ebpα (96%) as well as markers aP2 (93%) and oil red O staining (80%). Col3a1-/- 3T3-L1 cells displayed reduced cell adhesion, sustained active ß-catenin and deregulation of fibronectin (Fn) and collagen (Col4a1, Col6a1) extracellular matrix gene transcripts. Col3a1-/- 3T3-L1 cells also had dramatically reduced actin stress fibres. We conclude that ColIII is required for 3T3-L1 preadipocyte adipogenesis as well as the formation of actin stress fibres. The phenotype of Col3a1-/- 3T3-L1 cells is very similar to that of Gpr56-/- 3T3-L1 cells, suggesting a functional relationship between ColIII and Gpr56 in preadipocytes.

Adhesion G-protein coupled receptor 56 is required for 3T3-L1 adipogenesis.

Obesity-associated conditions represent major global health and financial burdens and understanding processes regulating adipogenesis could lead to novel intervention strategies. This study shows that adhesion G-protein coupled receptor 56 (GPR56) gene transcripts are reduced in abdominal visceral white adipose tissue derived from obese Zucker rats versus lean controls. Immunostaining in 3T3-L1 preadipocytes reveals both mitotic cell restricted surface and low level general expression patterns of Gpr56. Gpr56 transcripts are differentially expressed in 3T3-L1 cells during adipogenesis. Transient knockdown (KD) of Gpr56 in 3T3-L1 cells dramatically inhibits differentiation through reducing the accumulation of both neutral cellular lipids (56%) and production of established adipogenesis Pparγ 2 (60-80%), C/ebpα (40-78%) mediator, and Ap2 (56-80%) marker proteins. Furthermore, genome editing of Gpr56 in 3T3-L1 cells created CW2.2.4 and RM4.2.5.5 clones (Gpr56 -/- cells) with compound heterozygous deletion frameshift mutations which abolish adipogenesis. Genome edited cells have sustained levels of the adipogenesis inhibitor ß-catenin, reduced proliferation, reduced adhesion, altered profiles, and or abundance of extracellular matrix component gene transcripts for fibronectin, types I, III, and IV collagens and loss of actin stress fibers. ß-catenin KD alone is insufficient to restore adipogenesis in Gpr56 -/- cells. Together these data show that Gpr56 is required for adipogenesis in 3T3-L1 cells. This report is the first demonstration that Gpr56 participates in regulation of the adipogenesis developmental program. Modulation of the levels of this protein and/or its biological activity may represent a novel target for development of therapeutic agents for the treatment of obesity.

Criteria for Using INSURE in Management of Premature Babies with Respiratory Distress Syndrome.

INTRODUCTION: Respiratory distress syndrome (RDS) is defined as acute respiratory distress caused by surfactant deficiency that disturbs gas exchange in preterm infants. It is one of the most common neonatal problems and has been considered to be the most common cause of mortality and morbidity in preterm babies. AIM: In this study, different variables were studied to predict factors for INSURE failure that might help in choosing infants for this procedure early. METHODS: Sixty three (63) patients were enrolled in this study as they met the inclusion criteria. All neonates were intubated briefly less than 2 hours, given natural surfactant in the dose of 3 ml/kg. As soon as it was appropriate and the neonate was stable in the form of normal heart rate and oxygenation, extubation was done and the baby connected to NCPAP at a pressure of 6 cmH2O. INSURE failure was considered if the patient needed mechanical ventilation for more than 72 hours while INSURE success was considered if we were able to wean the patient from CPAP or if the patient didn't need mechanical ventilation in the first 72 hours after surfactant administration. The indications for mechanical ventilation after INSURE procedure were respiratory distress with desaturation (02 sat less than 90%), recurrent apnea, Pco2 more than 60 mmHg. RESULTS: Since INSURE procedure is being largely applied in the neonatal intensive care units, it is important to determine the candidate neonate for this procedure with the minimum failure rate. Although the sample of our study is small, but we can suggest that neonate with gestational age less than 28, birth weight less than 1000 gm, umbilical PH of less than 7, low Apgar score and anemic patients are at high risk for INSURE failure. CONCLUSION: Early diagnosis of PDA and IVH is essential to avoid INSURE method in these patients.

Discovery of Functional Motifs from the Interface Region of Oligomeric Proteins Using Frequent Subgraph Mining.

Modeling the interface region of a protein complex paves the way for understanding its dynamics and functionalities. Existing works model the interface region of a complex by using different approaches, such as, the residue composition at the interface region, the geometry of the interface residues, or the structural alignment of interface regions. These approaches are useful for ranking a set of docked conformation or for building scoring function for protein-protein docking, but they do not provide a generic and scalable technique for the extraction of interface patterns leading to functional motif discovery. In this work, we model the interface region of a protein complex by graphs and extract interface patterns of the given complex in the form of frequent subgraphs. To achieve this, we develop a scalable algorithm for frequent subgraph mining. We show that a systematic review of the mined subgraphs provides an effective method for the discovery of functional motifs that exist along the interface region of a given protein complex. In our experiments, we use three PDB protein structure datasets. The first two datasets are composed of PDB structures from different conformations of two dimeric protein complexes: HIV-1 protease (329 structures), and triosephosphate isomerase (TIM) (86 structures). The third dataset is a collection of different enzyme structures protein structures from the six top-level enzyme classes, namely: Oxydoreductase, Transferase, Hydrolase, Lyase, Isomerase, and Ligase. We show that for the first two datasets, our method captures the locking mechanism at the dimeric interface by taking into account the spatial positioning of the interfacial residues through graphs. Indeed, our frequent subgraph mining based approach discovers the patterns representing the dimerization lock which is formed at the base of the structure in 323 of the 329 HIV-1 protease structures. Similarly, for 86 TIM structures, our approach discovers the dimerization lock formation in 50 structures. For the enzyme structures, we show that we are able to capture the functional motifs (active sites) that are specific to each of the six top-level classes of enzymes through frequent subgraphs.

Context shapes: Efficient complementary shape matching for protein-protein docking.

We describe an efficient method for partial complementary shape matching for use in rigid protein-protein docking. The local shape features of a protein are represented using boolean data structures called Context Shapes. The relative orientations of the receptor and ligand surfaces are searched using precalculated lookup tables. Energetic quantities are derived from shape complementarity and buried surface area computations, using efficient boolean operations. Preliminary results indicate that our context shapes approach outperforms state-of-the-art geometric shape-based rigid-docking algorithms.