This educational clinical flowchart illustrates the diagnostic approach to a positive serum (1,3)-beta-D-glucan (BDG) result in patients with hematological malignancies. The algorithm begins by recommending a check for false positives, such as immunoglobulin administration, followed by repeated BDG testing and clinical risk assessment. The flowchart branches into four diagnostic pathways based on clinical suspicion, each integrated with representative CT imaging examples. The first branch focuses on blood cultures for candidemia. The second branch focuses on pulmonary involvement, showcasing lung CT scans illustrating a localized opacity (suggesting aspergillosis, fusariosis, or scedosporidiosis) and bilateral ground-glass opacities (suggesting Pneumocystis jirovecii pneumonia, PJP). The third branch addresses hepatosplenic candidiasis with an abdominal CT scan. The fourth branch focuses on fungal sinusitis, displaying a coronal sinus CT with mucosal thickening and opacification. Each imaging branch concludes with specific confirmatory tests, including galactomannan (GM), PCR, bronchoalveolar lavage (BAL), or tissue biopsy. This resource serves as a clinical guideline for managing invasive fungal infections in immunocompromised populations.

This diagnostic algorithm flowchart illustrates a machine-learning approach for human activity recognition (HAR) and smartphone location recognition (SLR). The diagram depicts a four-stage process: 1) User Mode input (Pocket, Talking, Texting, and Unknown); 2) Smartphone's accelerometer measurement, showing tri-axial (X, Y, Z) gravitational force data; 3) Deep Features Space, represented by an artificial neural network (ANN) architecture; and 4) Predicted Output Mode. The final stage displays a 2D cluster plot (latent space representation) that differentiates 'Known' activities (clustered data points in black, blue, green, and red) from 'Unknown' activities (cyan points). This methodology is used in clinical research for remote patient monitoring and mobile health (mHealth) applications to automatically detect and classify human physical activities or anomalies in movement patterns. The visualization demonstrates how deep learning features can achieve spatial separation between established behavioral patterns and novel or unknown movements, aiding in the development of robust gait and activity monitoring systems for physical therapy and rehabilitation.

Summary : This flowchart presents a clinical algorithm for the use of multimodality imaging in competitive athletes after pre-participation cardiovascular screening, based on medical history, physical exam, or ECG findings. flowchart: # Nodes : • MEDICAL HISTORY FINDINGS (rectangle): "Exertional Symptoms; Prior Unexplained Syncope; Family History of Genetic Heart Disease or Unexplained SCD" • PHYSICAL EXAMINATION FINDINGS (rectangle): "Pathologic Murmur; Hypertension +/- Asymmetry; Features Suggestive of Marfan Syndrome" • PATHOLOGIC ECG FINDINGS (rectangle): "Complete LBBB; T-Wave Inversions; Pathologic Q-waves; Biatrial Abnormality; ST-Segment Depression; RVH Voltage Criteria (with QRS Axis > 120°); LVH Voltage Criteria (with additional abnormal findings)" • TTE (rectangle, central): Transthoracic Echocardiogram (TTE) • Pathology Definitively Excluded by TTE (rectangle) • No Additional Imaging Indicated (rounded rectangle, green) • Pathology Inconclusive by TTE (rectangle) • Multimodality Diagnostic Approach (rounded rectangle, tan): "Cardiac MRI; Cardiac CT; Exercise +/- Imaging" • Pathology Definitively Identified by TTE (rectangle) • Confirmation & Risk Stratification (rounded rectangle, pink): "Cardiac MRI; Cardiac CT; Exercise +/- Imaging" # Connectors : • Arrows from each of the three initial findings boxes (Medical History, Physical Exam, ECG) point to TTE. • From TTE, three arrows branch: – To "Pathology Definitively Excluded by TTE" → then to "No Additional Imaging Indicated" – To "Pathology Inconclusive by TTE" → then to "Multimodality Diagnostic Approach" (with sub-arrows to Cardiac MRI, Cardiac CT, Exercise +/- Imaging) – To "Pathology Definitively Identified by TTE" → then to "Confirmation & Risk Stratification" (with sub-arrows to Cardiac MRI, Cardiac CT, Exercise +/- Imaging) • Logical "OR" labels between the three initial findings boxes. # Layout : • Three input boxes (Medical History, Physical Exam, ECG) arranged vertically on the left, each separated by "OR". • All three feed into a central TTE box. • TTE branches rightward into three possible outcomes, each leading to further action or no action. • Multimodality Diagnostic Approach and Confirmation & Risk Stratification are grouped on the right, each with three imaging options listed. # Analysis : • The algorithm starts with any abnormal finding in medical history, physical exam, or ECG, prompting a TTE. • If TTE excludes pathology, no further imaging is needed. • If TTE is inconclusive, a multimodality approach (MRI, CT, exercise imaging) is recommended. • If TTE identifies pathology, further imaging is used for confirmation and risk stratification. • The flowchart emphasizes a stepwise, evidence-driven escalation of imaging based on initial findings and TTE results.

Summary : This flowchart presents the European Society of Cardiology (ESC) 2023 algorithm for diagnosing cardiac device-related infective endocarditis (CIED-associated IE), outlining the stepwise approach from initial suspicion to definitive, possible, or rejected diagnosis, and subsequent recommended investigations. flowchart: # Nodes : • Suspected CIED-associated IE (rounded rectangle, top) • Baseline assessment and initial classification: clinical presentation + blood cultures + TTE + TOE (rounded rectangle, green) • ESC 2023 DIAGNOSTIC CRITERIA after IE (rounded rectangle, purple) • Three outcome nodes: DEFINITE (rounded rectangle, blue), POSSIBLE (rounded rectangle, blue), REJECTED (rounded rectangle, blue) • For POSSIBLE branch, three investigation nodes: – Repeat blood cultures if negative or doubtful; Repeat TTE/TOE within 5–7 days; PET/CT(A) to detect pocket infection +/- pulmonary embolism (rounded rectangle, green, Class I) – Add minor criteria: thoracic CT to detect septic pulmonary embolism/infarction (rounded rectangle, yellow, Class IIa) – PET/CT(A) to detect lead infection (rounded rectangle, orange, Class IIb) # Connectors : • Downward arrow from Suspected CIED-associated IE to Baseline assessment and initial classification. • Downward arrow from Baseline assessment to ESC 2023 DIAGNOSTIC CRITERIA after IE. • Three arrows from ESC 2023 DIAGNOSTIC CRITERIA after IE to DEFINITE, POSSIBLE, and REJECTED. • From POSSIBLE, downward arrows to each of the three investigation nodes. • Arrow from investigation nodes (Class I, IIa, IIb) back to DEFINITE node, indicating possible reclassification after further testing. # Layout : • Vertical flow from top (suspicion) to bottom (diagnosis and further testing). • Three branches from diagnostic criteria: left (DEFINITE), center (POSSIBLE with further investigations), right (REJECTED). • Investigation steps for POSSIBLE diagnosis are stacked vertically and color-coded by recommendation class (Class I: green, Class IIa: yellow, Class IIb: orange). • Feedback loop from further investigations to DEFINITE diagnosis. # Analysis : • The algorithm emphasizes a structured, stepwise approach: initial assessment with clinical, microbiological, and echocardiographic data, followed by application of ESC diagnostic criteria. • If diagnosis is POSSIBLE, additional investigations (repeat cultures, imaging) are recommended, with the possibility of upgrading to DEFINITE diagnosis. • Color-coding highlights the strength of recommendations for each investigation (Class I strongest, Class IIb weakest). • The flowchart visually reinforces the importance of repeated and multimodal testing in ambiguous cases, and provides clear decision pathways for clinicians.

This pathophysiology diagram illustrates the lifecycle of human neutrophils, from granulopoiesis in the bone marrow to blood circulation and tissue clearance. The diagram details the progression from Hematopoietic Stem Cells (HSC) and Granulocyte-Monocyte Progenitors (GMP) through maturation stages including pre-Neu, myelocyte, metamyelocyte, and band cells to mature neutrophils. Key surface marker profiles are provided for each stage, highlighting the dynamic expression of CD11b, CD16, CD10, CD33, and CD62L. A heatmap indicates the transition of CD66b, CD15, CD11b, CD16, and CD33 expression levels during differentiation. In the blood compartment, the diagram distinguishes between mature, immature, activated, and aged neutrophils, noting chemotactic receptors like CXCR2 (mobilization) and CXCR4 (homing/aging). The right side of the infographic shows neutrophil fates: margination in the lung, marrow, and liver; homeostatic tissue migration; and inflammatory migration mediated by ICAM-1. The lifecycle concludes with clearance by macrophages in the tissues or bone marrow, providing a comprehensive overview of neutrophil ontogeny, heterogeneity, and functional trafficking.

This composite diagnostic image displays a comparison chart of hematological cells from non-cancerous bone marrow aspirations, organized to show the maturation stages of the myeloid series. The visual consists of a grid with six columns, each representing a specific stage of development: Myeloblast (MB), Promyelocyte (PM), Myelocyte (M), Metamyelocyte (MM), Band (B), and Neutrophil (N). The cells are stained with Giemsa, revealing characteristic morphological changes in nuclear and cytoplasmic structure. Early stages (MB, PM) show large, round to oval nuclei with high nuclear-to-cytoplasmic (N:C) ratios and basophilic cytoplasm. As maturation progresses through the Myelocyte and Metamyelocyte stages, the nuclei become smaller and more indented. The Band (B) stage is characterized by an elongated, curved, non-segmented nucleus. The terminal stage, the Neutrophil (N), demonstrates distinct nuclear lobulation (typically 2-3 lobes) and increased cytoplasmic granularity. This image serves as an educational reference for identifying white blood cell precursors in the context of healthy hematopoiesis and chronic myelogenous leukemia (CML) classification.

This hematology infographic illustrates the metabolic reprogramming of neutrophils during homeostasis. The top panel depicts neutrophil development (granulopoiesis) from hematopoietic stem cells (HSCs) through various stages: Myeloblast (MB), Promyelocyte (PM), Myelocyte (MC), Metamyelocyte (MM), Band cell (BC), and segmented Neutrophil (SN). Morphological changes show nuclear progression from round/ovoid to indented and ultimately multi-lobed. The bottom panel highlights a significant immunometabolic shift. Immature stages (HSCs to Metamyelocytes) are predominantly reliant on mitochondrial respiration, utilizing fatty acids and glucose to fuel the TCA cycle and oxidative phosphorylation (OXPHOS) for ATP generation; this metabolic state sustains early cellular differentiation. In contrast, mature neutrophils (Band cells and SNs) transition to being predominantly reliant on glycolysis. In these mature cells, glucose and glycogen fuel the pentose phosphate pathway and glycolysis to produce ATP, which sustains effector functions including chemotaxis, ROS production, apoptosis, and NET formation. The diagram emphasizes the shift from high mitochondrial activity in progenitors to glycolytic dominance in terminally differentiated innate immune cells.

A multi-panel figure containing diagnostic imaging, an electrocardiogram (ECG), and a blood smear. Panel A presents axial PET/CT fusion and CT images of the thoracic and upper abdominal regions, showing a soft tissue mass near the esophageal hiatus and retroperitoneal abdominal trunk with hypermetabolic fluorodeoxyglucose (FDG) uptake, indicating tumor recurrence or metastasis. Panel B displays a 12-lead ECG strip demonstrating arrhythmias including premature atrial contractions (PACs) and premature ventricular contractions (PVCs). Green arrows highlight mild ST-segment depression in leads V5 and V6, while the blue arrow points to significant T-wave morphology changes, suggesting immunotherapy-related myocardial injury. Panel C is a peripheral blood smear micrograph showing two neutrophils with a left shift in nuclei (indicated by blue arrows), characterized by immature band forms, which is suggestive of an acute inflammatory response or infection. This composite illustrates the clinical monitoring of a patient with esophageal squamous cell carcinoma experiencing immune-related adverse events.