Dielectrophoretic enrichment of live chemo-resistant circulating-like pancreatic cancer cells from media of drug-treated adherent cultures of solid tumors

Due to low numbers of circulating tumor cells (CTCs) in liquid biopsies, there is much interest in enrichment of alternative circulating-like mesenchymal cancer cell subpopulations from in vitro tumor cultures for utilization within molecular profiling and drug screening. Viable cancer cells that are released into the media of drug-treated adherent cancer cell cultures exhibit anoikis resistance or anchorage-independent survival away from their extracellular matrix with nutrient sources and waste sinks, which serves as a pre-requisite for metastasis. The enrichment of these cell subpopulations from tumor cultures can potentially serve as an in vitro source of circulating-like cancer cells with greater potential for scale-up in comparison with CTCs. However, these live circulating-like cancer cell subpopulations exhibit size overlaps with necrotic and apoptotic cells in the culture media, which makes it challenging to selectively enrich them, while maintaining them in their suspended state. We present optimization of a flowthrough high frequency (1 MHz) positive dielectrophoresis (pDEP) device with sequential 3D field non-uniformities that enables enrichment of the live chemo-resistant circulating cancer cell subpopulation from an in vitro culture of metastatic patient-derived pancreatic tumor cells. Central to this strategy is the utilization of single-cell impedance cytometry with gates set by supervised machine learning, to optimize the frequency for pDEP, so that live circulating cells are selected based on multiple biophysical metrics, including membrane physiology, cytoplasmic conductivity and cell size, which is not possible using deterministic lateral displacement that is solely based on cell size. Using typical drug-treated samples with low levels of live circulating cells (<3%), we present pDEP enrichment of the target subpopulation to ∼44% levels within 20 minutes, while rejecting >90% of dead cells. This strategy of utilizing single-cell impedance cytometry to guide the optimization of dielectrophoresis has implications for other complex biological samples.

Correlating Antibiotic-Induced Dysbiosis to Clostridioides difficile Spore Germination and Host Susceptibility to Infection Using an Ex Vivo Assay

Antibiotic-induced microbiota disruption and its persistence create conditions for dysbiosis and colonization by opportunistic pathogens, such as those causing Clostridioides difficile (C. difficile) infection (CDI), which is the most severe hospital-acquired intestinal infection. Given the wide differences in microbiota across hosts and in their recovery after antibiotic treatments, there is a need for assays to assess the influence of dysbiosis and its recovery dynamics on the susceptibility of the host to CDI. Germination of C. difficile spores is a key virulence trait for the onset of CDI, which is influenced by the level of primary vs secondary bile acids in the intestinal milieu that is regulated by the microbiota composition. Herein, the germination of C. difficile spores in fecal supernatant from mice that are subject to varying degrees of antibiotic treatment is utilized as an ex vivo assay to predict intestinal dysbiosis in the host based on their susceptibility to CDI, as determined by in vivo CDI metrics in the same mouse model. Quantification of spore germination down to lower detection limits than the colony-forming assay is achieved by using impedance cytometry to count single vegetative bacteria that are identified based on their characteristic electrical physiology for distinction vs aggregated spores and cell debris in the media. As a result, germination can be quantified at earlier time points and with fewer spores for correlation to CDI outcomes. This sets the groundwork for a point-of-care tool to gauge the susceptibility of human microbiota to CDI after antibiotic treatments.

Multichannel impedance cytometry downstream of cell separation by deterministic lateral displacement to quantify macrophage enrichment in heterogeneous samples

The integration of on-chip biophysical cytometry downstream of microfluidic enrichment for inline monitoring of phenotypic and separation metrics at single-cell sensitivity can allow for active control of separation and its application to versatile sample sets. We present integration of impedance cytometry downstream of cell separation by deterministic lateral displacement (DLD) for enrichment of activated macrophages from a heterogeneous sample, without the problems of biased sample loss and sample dilution caused by off-chip analysis. This required designs to match cell/particle flow rates from DLD separation into the confined single-cell impedance cytometry stage, the balancing of flow resistances across the separation array width to maintain unidirectionality, and the utilization of co-flowing beads as calibrated internal standards for inline assessment of DLD separation and for impedance data normalization. Using a heterogeneous sample with un-activated and activated macrophages, wherein macrophage polarization during activation causes cell size enlargement, on-chip impedance cytometry is used to validate DLD enrichment of the activated subpopulation at the displaced outlet, based on the multiparametric characteristics of cell size distribution and impedance phase metrics. This hybrid platform can monitor separation of specific subpopulations from cellular samples with wide size distributions, for active operational control and enhanced sample versatility.

Supervised learning on impedance cytometry data for label-free biophysical distinction of pancreatic cancer cells versus their associated fibroblasts under gemcitabine treatment

Chemotherapy failure in pancreatic cancer patients is widely attributed to cancer cell reprogramming towards drug resistance by cancer associated fibroblasts (CAFs), which are the abundant cell type in the tumor microenvironment. Association of drug resistance to specific cancer cell phenotypes within multicellular tumors can advance isolation protocols for enabling cell-type specific gene expression markers to identify drug resistance. This requires the distinction of drug resistant cancer cells versus CAFs, which is challenging since permeabilization of CAF cells during drug treatment can cause non-specific uptake of cancer cell-specific stains. Cellular biophysical metrics, on the other hand, can provide multiparametric information to assess the gradual alteration of target cancer cells towards drug resistance, but these phenotypes need to be distinguished versus CAFs. Using pancreatic cancer cells and CAFs from a metastatic patient-derived tumor that exhibits cancer cell drug resistance under CAF co-culture, the biophysical metrics from multifrequency single-cell impedance cytometry are utilized for distinction of the subpopulation of viable cancer cells versus CAFs, before and after gemcitabine treatment. This is accomplished through supervised machine learning after training the model using key impedance metrics for cancer cells and CAFs from transwell co-cultures, so that an optimized classifier model can recognize each cell type and predict their respective proportions in multicellular tumor samples, before and after gemcitabine treatment, as validated by their confusion matrix and flow cytometry assays. In this manner, an aggregate of the distinguishing biophysical metrics of viable cancer cells after gemcitabine treatment in co-cultures with CAFs can be used in longitudinal studies, to classify and isolate the drug resistant subpopulation for identifying markers.

Automated biophysical classification of apoptotic pancreatic cancer cell subpopulations by using machine learning approaches with impedance cytometry

Unrestricted cell death can lead to an immunosuppressive tumor microenvironment, with dysregulated apoptotic signaling that causes resistance of pancreatic cancer cells to cytotoxic therapies. Hence, modulating cell death by distinguishing the progression of subpopulations under drug treatment from viable towards early apoptotic, late apoptotic, and necrotic states is of interest. While flow cytometry after fluorescent staining can monitor apoptosis with single-cell sensitivity, the background of non-viable cells within non-immortalized pancreatic tumors from xenografts can confound distinction of the intensity of each apoptotic state. Based on single-cell impedance cytometry of drug-treated pancreatic cancer cells that are obtained from tumor xenografts with differing levels of gemcitabine sensitivity, we identify the biophysical metrics that can distinguish and quantify cellular subpopulations at the early apoptotic versus late apoptotic and necrotic states, by using machine learning methods to train for the recognition of each phenotype. While supervised learning has previously been used for classification of datasets with known classes, our advancement is the utilization of optimal positive controls for each class, so that clustering by unsupervised learning and classification by supervised learning can occur on unknown datasets, without human interference or manual gating. In this manner, automated biophysical classification can be used to follow the progression of apoptotic states in each heterogeneous drug-treated sample, for developing drug treatments to modulate cancer cell death and advance longitudinal analysis to discern the emergence of drug resistant phenotypes.

Modified Red Blood Cells as Multimodal Standards for Benchmarking Single-Cell Cytometry and Separation Based on Electrical Physiology

Biophysical cellular information at single-cell sensitivity is becoming increasingly important within analytical and separation platforms that associate the cell phenotype with markers of disease, infection, and immunity. Frequency-modulated electrically driven microfluidic measurement and separation systems offer the ability to sensitively identify single cells based on biophysical information, such as their size and shape, as well as their subcellular membrane morphology and cytoplasmic organization. However, there is a lack of reliable and reproducible model particles with well-tuned subcellular electrical phenotypes that can be used as standards to benchmark the electrical physiology of unknown cell types or to benchmark dielectrophoretic separation metrics of novel device strategies. Herein, the application of red blood cells (RBCs) as multimodal standard particles with systematically modulated subcellular electrophysiology and associated fluorescence level is presented. Using glutaraldehyde fixation to vary membrane capacitance and by membrane resealing after electrolyte penetration to vary interior cytoplasmic conductivity and fluorescence in a correlated manner, each modified RBC type can be identified at single-cell sensitivity based on phenomenological impedance metrics and fitted to dielectric models to compute biophysical information. In this manner, single-cell impedance data from unknown RBC types can be mapped versus these model RBC types for facile determination of subcellular biophysical information and their dielectrophoretic separation conditions, without the need for time-consuming algorithms that often require unknown fitting parameters. Such internal standards for biophysical cytometry can advance in-line phenotypic recognition strategies.

Biophysical quantification of reorganization dynamics of human pancreatic islets during co-culture with adipose-derived stem cells

Reorganization dynamics of human islets during co-culture with adipose stem cells depends on islet size and the heterogeneity can be assessed based on biomechanical opacity of individual islets.

Apoptotic Bodies in the Pancreatic Tumor Cell Culture Media Enable Label-Free Drug Sensitivity Assessment by Impedance Cytometry

The ability to rapidly and sensitively predict drug response and toxicity using in vitro models of patient-derived tumors is essential for assessing chemotherapy efficacy. Currently, drug sensitivity assessment for solid tumors relies on imaging adherent cells or by flow cytometry of cells lifted from drug-treated cultures after fluorescent staining for apoptotic markers. Subcellular apoptotic bodies (ABs), including microvesicles that are secreted into the culture media under drug treatment can potentially serve as markers for drug sensitivity, without the need to lift cells under culture. However, their stratification to quantify cell disassembly is challenging due to their compositional diversity, with tailored labeling strategies currently needed for the recognition and cytometry of each AB type. It is shown that the high frequency impedance phase versus size distribution of ABs determined by high-throughput single-particle impedance cytometry of supernatants in the media of gemcitabine-treated pancreatic tumor cultures exhibits phenotypic resemblance to lifted apoptotic cells and enables shape-based stratification within distinct size ranges, which is not possible by flow cytometry. It is envisioned that this tool can be applied in conjunction with the appropriate pancreatic tumor microenvironment model to assess drug sensitivity and toxicity of patient-derived tumors, without the need to lift cells from cultures.

Self-aligned sequential lateral field non-uniformities over channel depth for high throughput dielectrophoretic cell deflection

Dielectrophoresis (DEP) enables the separation of cells based on subtle subcellular phenotypic differences by controlling the frequency of the applied field. However, current electrode-based geometries extend over a limited depth of the sample channel, thereby reducing the throughput of the manipulated sample (sub-μL min-1 flow rates and <105 cells per mL). We present a flow through device with self-aligned sequential field non-uniformities extending laterally across the sample channel width (100 μm) that are created by metal patterned over the entire depth (50 μm) of the sample channel sidewall using a single lithography step. This enables single-cell streamlines to undergo progressive DEP deflection with minimal dependence on the cell starting position, its orientation versus the field and intercellular interactions. Phenotype-specific cell separation is validated (>μL min-1 flow and >106 cells per mL) using heterogeneous samples of healthy and glutaraldehyde-fixed red blood cells, with single-cell impedance cytometry showing that the DEP collected fractions are intact and exhibit electrical opacity differences consistent with their capacitance-based DEP crossover frequency. This geometry can address the vision of an "all electric" selective cell isolation and cytometry system for quantifying phenotypic heterogeneity of cellular systems.

Label-Free Quantification of Cell Cycle Synchronicity of Human Neural Progenitor Cells Based on Electrophysiology Phenotypes

The ability to coax human-induced pluripotent stem cells (hiPSCs) into human neural progenitor cells (hNPCs) can lead to novel drug discovery and transplant therapy platforms for neurological diseases. Since hNPCs can form organoids that mimic brain development, there is emerging interest in their label-free characterization for controlling cell composition to optimize organoid formation in three-dimensional (3D) cultures. However, this requires the ability to quantify hNPCs in heterogeneous samples with subpopulations of similar phenotype. Using high-throughput (>6000 cells per condition), single-cell impedance cytometry, we present the utilization of electrophysiology for quantification of hNPC subpopulations that are altered in cell cycle synchronicity by camptothecin (CPT) exposure. Electrophysiology phenotypes are determined from impedance magnitude and phase metrics for distinguishing each cell cycle phase, as validated by flow cytometry, for a wide range of subpopulation proportions. Using multishell dielectric models for each cell cycle phase, electrophysiology alterations with CPT dose could be predicted. This label-free detection strategy can prevent loss of cell viability to speed the optimization of cellular compositions for organoid development.

Single-cell microfluidic impedance cytometry: from raw signals to cell phenotypes using data analytics

The biophysical analysis of single-cells by microfluidic impedance cytometry is emerging as a label-free and high-throughput means to stratify the heterogeneity of cellular systems based on their electrophysiology. Emerging applications range from fundamental life-science and drug assessment research to point-of-care diagnostics and precision medicine. Recently, novel chip designs and data analytic strategies are laying the foundation for multiparametric cell characterization and subpopulation distinction, which are essential to understand biological function, follow disease progression and monitor cell behaviour in microsystems. In this tutorial review, we present a comparative survey of the approaches to elucidate cellular and subcellular features from impedance cytometry data, covering the related subjects of device design, data analytics (i.e., signal processing, dielectric modelling, population clustering), and phenotyping applications. We give special emphasis to the exciting recent developments of the technique (timeframe 2017-2020) and provide our perspective on future challenges and directions. Its synergistic application with microfluidic separation, sensor science and machine learning can form an essential toolkit for label-free quantification and isolation of subpopulations to stratify heterogeneous biosystems.

Quantifying bacterial spore germination by single-cell impedance cytometry for assessment of host microbiota susceptibility to Clostridioides difficile infection

The germination of ingested spores is often a necessary first step required for enabling bacterial outgrowth and host colonization, as in the case of Clostridioides difficile (C. difficile) infection. Spore germination rate in the colon depends on microbiota composition and its level of disruption by antibiotic treatment since secretions by commensal bacteria modulate primary to secondary bile salt levels to control germination. Assessment of C. difficile spore germination typically requires measurement of colony-forming units, which is labor intensive and takes at least 24 h to perform but is regularly required due to the high recurrence rates of nosocomial antibiotic-associated diarrhea. We present a rapid method to assess spore germination by using high throughput single-cell impedance cytometry (>300 events/s) to quantify live bacterial cells, by gating for their characteristic electrophysiology versus spores, so that germination can be assessed after just 4 h of culture at a detection limit of ~100 live cells per 50 μL sample. To detect the phenotype of germinated C. difficile bacteria, we utilize its characteristically higher net conductivity versus that of spore aggregates and non-viable C. difficile forms, which causes a distinctive high-frequency (10 MHz) impedance phase dispersion within moderately conductive media (0.8 S/m). In this manner, we can detect significant differences in spore germination rates within just 4 h, with increasing primary bile salt levels in vitro and using ex vivo microbiota samples from an antibiotic-treated mouse model to assess susceptibility to C. difficile infection. We envision a rapid diagnostic tool for assessing host microbiota susceptibility to bacterial colonization after key antibiotic treatments.

Rapid in Vitro Assessment of Clostridioides difficile Inhibition by Probiotics Using Dielectrophoresis to Quantify Cell Structure Alterations

Clostridioides difficile (C. difficile) infection (CDI) is the primary cause of nosocomial antibiotic-associated diarrhea, with high recurrence rates following initial antibiotic treatment regimens. Restoration of the host gut microbiome through probiotic therapy is under investigation to reduce recurrence. Current in vitro methods to assess C. difficile deactivation by probiotic microorganisms are based on C. difficile growth inhibition, but the cumbersome and time-consuming nature of the assay limits the number of assessed permutations. Phenotypic alterations to the C. difficile cellular structure upon interaction with probiotics can potentially enable rapid assessment of the inhibition without the need for extended culture. Because supernatants from cultures of commensal microbiota reflect the complex metabolite milieu that deactivates C. difficile, we explore coculture of C. difficile with an optimal dose of supernatants from probiotic culture to speed growth inhibition assays and enable correlation with alterations to its prolate ellipsoidal structure. Based on sensitivity of electrical polarizability to C. difficile cell shape and subcellular structure, we show that the inhibitory effect of Lactobacillus spp. supernatants on C. difficile can be determined based on the positive dielectrophoresis level within just 1 h of culture using a highly toxigenic strain and a clinical isolate, whereas optical and growth inhibition measurements require far greater culture time. We envision application of this in vitro coculture model, in conjunction with dielectrophoresis, to rapidly screen for potential probiotic combinations for the treatment of recurrent CDI.

A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry

Microfluidic applications such as active particle sorting or selective enrichment require particle classification techniques that are capable of working in real time. In this paper, we explore the use of neural networks for fast label-free particle characterization during microfluidic impedance cytometry. A recurrent neural network is designed to process data from a novel impedance chip layout for enabling real-time multiparametric analysis of the measured impedance data streams. As demonstrated with both synthetic and experimental datasets, the trained network is able to characterize with good accuracy size, velocity, and cross-sectional position of beads, red blood cells, and yeasts, with a unitary prediction time of 0.4 ms. The proposed approach can be extended to other device designs and cell types for electrical parameter extraction. This combination of microfluidic impedance cytometry and machine learning can serve as a stepping stone to real-time single-cell analysis and sorting. Graphical Abstract.

High-throughput dynamical analysis of dielectrophoretic frequency dispersion of single cells based on deflected flow streamlines

Phenotypic quantification of cells based on their plasma membrane capacitance and cytoplasmic conductivity, as determined by their dielectrophoretic frequency dispersion, is often used as a marker for their biological function. However, due to the prevalence of phenotypic heterogeneity in many biological systems of interest, there is a need for methods capable of determining the dielectrophoretic dispersion of single cells at high throughput and without the need for sample dilution. We present a microfluidic device methodology wherein localized constrictions in the microchannel are used to enhance the field delivered by adjoining planar electrodes, so that the dielectrophoresis level and direction on flow-focused cells can be determined on each traversing cell in a high-throughput manner based on their deflected flow streamlines. Using a sample of human red blood cells diluted to 2.25 × 10⁸ cells/mL, the dielectrophoretic translation of single cells traversing at a flow rate of 1.68 μL/min is measured at a throughput of 1.1 × 10⁵ cells/min, to distinguish positive versus negative dielectrophoresis and determine their crossover frequency in media of differing conductivity for validation of the computed membrane capacitance to that from prior methods. We envision application of this dynamic dielectrophoresis (Dy-DEP) method towards high-throughput measurement of the dielectric dispersion of single cells to stratify phenotypic heterogeneity of a particular sample based on their DEP crossover frequency, without the need for significant sample dilution. Grapical abstract.

Electrophysiology-based stratification of pancreatic tumorigenicity by label-free single-cell impedance cytometry

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer lacking specific biomarkers that can be correlated to disease onset, promotion and progression. To assess whether tumor cell electrophysiology may serve as a marker for PDAC tumorigenicity, we use multi-frequency impedance cytometry at high throughput (∼350 cells/s) to measure the electrical phenotype of single PDAC tumor cells from xenografts, which are derived from primary pancreatic tumors versus those from liver metastases of different patients. A novel phase contrast metric based on variations in the high and low frequency impedance phase responses that is related to electrophysiology of the cell interior is found to be systematically altered as a function of tumorigenicity. PDAC cells of higher tumorigenicity exhibited lowered interior conductivity and enhanced permittivity, which is validated by the dielectrophoresis on the respective cell types. Using genetic analysis, we suggest the role of dysregulated Na⁺ transport and removal of Ca²⁺ ions from the cytoplasm on key oncogenic KRAS-driven processes that may be responsible for lowering of the interior cell conductivity. We envision that impedance cytometry can serve as a tool to quantify phenotypic heterogeneity for rapidly stratifying tumorigenicity. It can also aid in protocols for dielectrophoretic isolation of cells with a particular phenotype for prognostic studies on patient survival and to tailor therapy selection to specific patients.

AC electrokinetic biased deterministic lateral displacement for tunable particle separation

We describe a novel particle separation technique that combines deterministic lateral displacement (DLD) with orthogonal electrokinetic forces. DLD is a microfluidic technique for continuous flow particle separation based on size. We describe new tunable devices that use a combination of AC electric fields with DLD to separate particles below the critical diameter. Planar electrodes were integrated into a classical DLD device to produce a force orthogonal to the fluid flow direction. Experiments with 3.0 μm, 1.0 μm and 500 nm diameter microspheres show that at low frequencies (up to 500 Hz) particles oscillate in the direction of the field due to electrophoretic (EP)/electroosmotic (EO) forces. As the frequency of the field increases, the amplitude of these oscillations vanishes and, eventually dielectrophoresis (DEP) becomes the dominant electrokinetic force on the particles (DEP arises from electric field inhomogeneities caused by the presence of the DLD posts). Both mechanisms alter the paths of the particles inside the DLD devices leading to enhanced sorting of particles below the critical diameter of the device.

Dielectric characterization of Plasmodium falciparum-infected red blood cells using microfluidic impedance cytometry

Although malaria is the world's most life-threatening parasitic disease, there is no clear understanding of how certain biophysical properties of infected cells change during the malaria infection cycle. In this article, we use microfluidic impedance cytometry to measure the dielectric properties of Plasmodium falciparum-infected red blood cells (i-RBCs) at specific time points during the infection cycle. Individual parasites were identified within i-RBCs using green fluorescent protein (GFP) emission. The dielectric properties of cell sub-populations were determined using the multi-shell model. Analysis showed that the membrane capacitance and cytoplasmic conductivity of i-RBCs increased along the infection time course, due to membrane alterations caused by parasite infection. The volume ratio occupied by the parasite was estimated to vary from less than 10% at earlier stages, to approximately 90% at later stages. This knowledge could be used to develop new label-free cell sorting techniques for sample pre-enrichment, improving diagnosis.

Analysis of Parasitic Protozoa at the Single-cell Level using Microfluidic Impedance Cytometry

At present, there are few technologies which enable the detection, identification and viability analysis of protozoan pathogens including Cryptosporidium and/or Giardia at the single (oo)cyst level. We report the use of Microfluidic Impedance Cytometry (MIC) to characterise the AC electrical (impedance) properties of single parasites and demonstrate rapid discrimination based on viability and species. Specifically, MIC was used to identify live and inactive C. parvum oocysts with over 90% certainty, whilst also detecting damaged and/or excysted oocysts. Furthermore, discrimination of Cryptosporidium parvum, Cryptosporidium muris and Giardia lamblia, with over 92% certainty was achieved. Enumeration and identification of (oo)cysts can be achieved in a few minutes, which offers a reduction in identification time and labour demands when compared to existing detection methods.

Tongue reduction in Beckwith-Weidemann syndrome

OBJECTIVE

To review our experience with patients with macroglossia as a component of Beckwith-Weidemann Syndrome (BWS).

DESIGN

Chart review of six patients treated with BWS.

SETTING

Tertiary care teaching hospital.

PATIENTS

Six patients diagnosed with BWS and macroglossia.

INTERVENTIONS

Four patients underwent at least one surgical procedure to address their macroglossia. The surgical options and potential complications are discussed.

RESULTS

Three patients who have undergone tongue reduction have a functioning tongue with normal mobility. Two patients have required tracheotomy as apart of their management and still have significant tongue enlargement.

CONCLUSIONS

Macroglossia as a part of BWS may present a difficult management problem. Various methods of tongue reductions have been reported with mixed results.

Pediatric temporal bone fractures

OBJECTIVE

To examine the etiology, presentation, and management of temporal bone fractures in children.

STUDY DESIGN

Case control.

METHOD

Retrospective review of a level I pediatric trauma center from July 1, 1990 to November 1, 1996 identified 680 patients. Inclusion criteria of age less than 14 years and only blunt temporal bone trauma identified 122 patients, with 97 charts available for review. The criteria for temporal bone fracture consisted of both clinical and radiologic information. Only patients with temporal bone fractures confirmed by computed tomography, a complete otolaryngology examination, and audiometric evaluations were included in the study. The data were analyzed with the Kruskal-Wallis analysis of variance (ANOVA) for examining the three separate age groups of fractures. Chi-squared analysis was used to compare these data with previously published adult and pediatric temporal bone fracture series and to examine the three separate age groups of fractures.

RESULTS

The review identified 72 children with 79 temporal bone fractures: 47 boys and 25 girls. The patients ranged from 6 months to 14 years of age, with a bimodal distribution of patients with peaks at 3 years and 12 years of age. The most common causes of fractures were motor vehicle accidents (47%), falls (40%), biking accidents (8%), and blows to the head (7%). Common presenting signs and symptoms included hearing loss (82%), hemotympanum (81%), loss of consciousness (63%), intracranial injuries (58%), bloody otorrhea (58%), extremity fractures (8%), and facial nerve weakness (3%). The most common causes of temporal bone fractures were falls and motor vehicle accidents. Forty-two patients were noted to have bloody otorrhea and possible cerebrospinal fluid leak. Twenty-four received intravenous antibiotics. No patient developed prolonged otorrhea or meningitis during hospitalization and the follow-up period. The classification of fracture patterns was longitudinal, 54%; transverse, 6%; oblique, 10%; squamous, 27%; and other, 3%. Hearing loss was found in 59 patients, with conductive hearing loss being the most common finding in 56% of the patients, followed by sensorineural hearing loss in 17% and mixed hearing loss in 10%.

CONCLUSIONS

Pediatric temporal bone fractures are associated with falls and motor vehicle accidents. There is a high incidence of associated intracranial injuries and hearing loss, but facial nerve injuries are uncommon. Timely management minimizes complications.