QuantiMus: A Machine Learning-Based Approach for High Precision Analysis of Skeletal Muscle Morphology.

Skeletal muscle injury provokes a regenerative response, characterized by the de novo generation of myofibers that are distinguished by central nucleation and re-expression of developmentally restricted genes. In addition to these characteristics, myofiber cross-sectional area (CSA) is widely used to evaluate muscle hypertrophic and regenerative responses. Here, we introduce QuantiMus, a free software program that uses machine learning algorithms to quantify muscle morphology and molecular features with high precision and quick processing-time. The ability of QuantiMus to define and measure myofibers was compared to manual measurement or other automated software programs. QuantiMus rapidly and accurately defined total myofibers and measured CSA with comparable performance but quantified the CSA of centrally-nucleated fibers (CNFs) with greater precision compared to other software. It additionally quantified the fluorescence intensity of individual myofibers of human and mouse muscle, which was used to assess the distribution of myofiber type, based on the myosin heavy chain isoform that was expressed. Furthermore, analysis of entire quadriceps cross-sections of healthy and mdx mice showed that dystrophic muscle had an increased frequency of Evans blue dye+ injured myofibers. QuantiMus also revealed that the proportion of centrally nucleated, regenerating myofibers that express embryonic myosin heavy chain (eMyHC) or neural cell adhesion molecule (NCAM) were increased in dystrophic mice. Our findings reveal that QuantiMus has several advantages over existing software. The unique self-learning capacity of the machine learning algorithms provides superior accuracy and the ability to rapidly interrogate the complete muscle section. These qualities increase rigor and reproducibility by avoiding methods that rely on the sampling of representative areas of a section. This is of particular importance for the analysis of dystrophic muscle given the "patchy" distribution of muscle pathology. QuantiMus is an open source tool, allowing customization to meet investigator-specific needs and provides novel analytical approaches for quantifying muscle morphology

Dental Anxiety and Fear among Patients in Jazan, Kingdom of Saudi Arabia: A Cross-sectional Study

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Transient receptor potential vanilloid 4 channel deficiency aggravates tubular damage after acute renal ischaemia reperfusion

Transient receptor potential vanilloid 4 (TRPV4) cation channels are functional in all renal vascular segments and mediate endothelium-dependent vasorelaxation. Moreover, they are expressed in distinct parts of the tubular system and activated by cell swelling. Ischaemia/reperfusion injury (IRI) is characterized by tubular injury and endothelial dysfunction. Therefore, we hypothesised a putative organ protective role of TRPV4 in acute renal IRI. IRI was induced in TRPV4 deficient (Trpv4 KO) and wild-type (WT) control mice by clipping the left renal pedicle after right-sided nephrectomy. Serum creatinine level was higher in Trpv4 KO mice 6 and 24 hours after ischaemia compared to WT mice. Detailed histological analysis revealed that IRI caused aggravated renal tubular damage in Trpv4 KO mice, especially in the renal cortex. Immunohistological and functional assessment confirmed TRPV4 expression in proximal tubular cells. Furthermore, the tubular damage could be attributed to enhanced necrosis rather than apoptosis. Surprisingly, the percentage of infiltrating granulocytes and macrophages were comparable in IRI-damaged kidneys of Trpv4 KO and WT mice. The present results suggest a renoprotective role of TRPV4 during acute renal IRI. Further studies using cell-specific TRPV4 deficient mice are needed to clarify cellular mechanisms of TRPV4 in IRI

QuantiMus: A Machine Learning-Based Approach for High Precision Analysis of Skeletal Muscle Morphology

Skeletal muscle injury provokes a regenerative response, characterized by the de novo generation of myofibers that are distinguished by central nucleation and re-expression of developmentally restricted genes. In addition to these characteristics, myofiber crosssectional area (CSA) is widely used to evaluate muscle hypertrophic and regenerative responses. Here, we introduce QuantiMus, a free software program that uses machine learning algorithms to quantify muscle morphology and molecular features with high precision and quick processing-time. The ability of QuantiMus to define and measure myofibers was compared to manual measurement or other automated software programs. QuantiMus rapidly and accurately defined total myofibers and measured CSA with comparable performance but quantified the CSA of centrally-nucleated fibers (CNFs) with greater precision compared to other software. It additionally quantified the fluorescence intensity of individual myofibers of human and mouse muscle, which was used to assess the distribution of myofiber type, based on the myosin heavy chain isoform that was expressed. Furthermore, analysis of entire quadriceps cross-sections of healthy and mdx mice showed that dystrophic muscle had an increased frequency of Evans blue dye+ injured myofibers. QuantiMus also revealed that the proportion of centrally nucleated, regenerating myofibers that express embryonic myosin heavy chain (eMyHC) or neural cell adhesion molecule (NCAM) were increased in dystrophic mice. Our findings reveal that QuantiMus has several advantages over existing software. The unique self-learning capacity of the machine learning algorithms provides superior accuracy and the ability to rapidly interrogate the complete muscle section. These qualities increase rigor and reproducibility by avoiding methods that rely on the sampling of representative areas of a section. This is of particular importance for the analysis of dystrophic muscle given the “patchy” distribution of muscle pathology. QuantiMus is an open source tool, allowing customization to meet investigatorspecific needs and provides novel analytical approaches for quantifying muscle morphology

Application of Artificial Intelligence (AI) in Records Management and Systems

The study aimed to clarify the concept of artificial intelligence and identify its potential roles in improving records management and systems, highlight its most prominent techniques that contribute to the effectiveness and efficiency of records management processes, identify the challenges and concerns facing the application of artificial intelligence techniques in records management and systems, and seek to find solutions and proposals. appropriate to address these fears and challenges. The study used the inductive approach by reviewing relevant scientific sources. The study reached several results, the most important of which is that artificial intelligence techniques such as machine learning, natural language processing, and image recognition can contribute significantly to improving the efficiency and effectiveness of records management in various fields. These technologies enable records to be classified more accurately, speed up search and retrieval processes, and analyze data in greater depth. And access to the most prominent challenges and concerns that must be addressed when applying artificial intelligence in records management. The most prominent of these are issues of privacy, security, and legal liability. The study presented many recommendations, the most important of which are: the need to provide an appropriate infrastructure to integrate artificial intelligence technology with records management, with the need to adopt innovative systems based on artificial intelligence to improve records management, and records management specialists must keep pace with developments in the field of artificial intelligence, with the recommendation to conduct more In-depth scientific studies on the application of artificial intelligence in records management

Linear programs and convex hulls over fields of puiseux fractions

We describe the implementation of a subfield of the field of formal Puiseux series in polymake. This is employed for solving linear programs and computing convex hulls depending on a real parameter. Moreover, this approach is also useful for computations in tropical geometry

Beachrock as a Paleoshoreline Indicator: Example from Wadi Al-Hamd, South Al-Wajh, Saudi Arabia

The present study concerns the Holocene inland beachrocks that are exposed in the Red Sea coastal plain at the mouth of Wadi Al-Hamd, South Al-Wajh City, Saudi Arabia, and their utility as an indicator for Holocene climate and sea level changes. In addition, the framework composition, and carbon and oxygen isotopic data, are employed to interpret the origin of their cement. The beachrock consists mainly of gravel and coarse-grained terrigenous sediments dominated by lithic fragments of volcanic rocks, cherts and rare limestones along with quartz, feldspars and traces of amphiboles and heavy minerals. In addition, rare skeletal remains dominated by coralline algae, benthic foraminifera and mollusca remains are recognized. The allochems are cemented by high Mg-calcite (HMC) formed mainly in the intertidal zone under active marine phreatic conditions. The cement takes the form of isopachous to anisopachous rinds of bladed crystals, micritic rim non-selectively surrounding siliciclastic and skeletal remains, and pore-filling micrite. Pore-filling micrite cement occasionally displays a meniscus fabric, suggesting a vadose environment. The δ18O and δ13C values of carbonate cement range from −0.35‰ to 1‰ (mean 0.25‰) and −0.09‰ to 3.03‰ (mean 1.85‰), respectively, which are compatible with precipitation from marine waters. The slight depletion in δ18O and δ13C values in the proximal sample may suggest a slight meteoric contribution

Contamination Evaluation of Heavy Metals in a Sediment Core from the Al-Salam Lagoon, Jeddah Coast, Saudi Arabia

The Al-Salam Lagoon is one of the recreational sites along the Jeddah coast, showing the environmental impacts of urbanization along the coast. A sediment core (220 cm) was collected from the intertidal zone to evaluate the heavy metals (Fe, Mn, Cr, Ni, Cu, Zn, and Pb) and geochemical indices (contamination factor, geo-accumulation index, and pollution load index). In the organ-ic-rich muddy sediments (0–100 cm), there is a high metals content and a pollution load index of ~3, indicting anthropogenic impacts with high Cu contamination (CF:12) and moderate Fe, Mn, Cr, Ni, Zn, and Pb contamination (CF: <3). The organic matter and heavy metals washed through surface run-off from the land and deposited as urban waste. Down the core, consistent metals concentration, CF, and Igeo trends indicate a common pollutant source and pollution load variations over time. In the sediment section (70–40 cm), a high organic matter, metal concentration, CF, Igeo, and PLI value (≥5) suggest an uncontrolled pollution load. The decreased and stable trends of environmental indicators toward surface sediments suggest measures taken to control the pollution along the Jeddah coast. Below 110 cm, the carbonate-rich sediments have low organic matter and metals, showing an unpolluted depositional environment. The negative geo-accumulation index implies a geogenic source and indicates no anthropogenic impacts as inferred from low (~1.0) CF and PLI

Physiological potential of cytokines and liver damages

Cytokines are soluble extracellular small molecular weight protein or peptide. They are produced by virtually every nucleated cell type in response to injurious stimuli to control body metabolism, infection, inflammation and tissue or neuronal damage; therefore acting as messengers between tissues and the immune system; and participating in many physiological processes through their either anti-inflammatory or pro-inflammatory characteristics. Many cytokines have multiple cellular sources and targets, as well as many natural inducers and inhibitors. In pathophysiological conditions and during the early phase of chronic liver diseases, agent like virus, bacteria, parasites, ethanol, or toxins, induce secretion of cytokines at high levels. The presence of cytokine antagonists and soluble cytokine receptors, often released in concert with their respective cytokine agonist, presents additional complexity to interpretation