Early Detection Of Infection Research Articles

A-356 A hand-held microfluidic platform for rapid and sensitive identification of respiratory viruses from nasal swab samples

Abstract Background Identifying the infected individual with a fast and accurate test is essential to constrain the spread of viruses and provide the proper treatment. Antigen rapid test (ART) provides quick screening of infection for patients; however, it shows less promising in the detection of early infection and patent’s full recovery as its inferior sensitivity to the nucleic acid detection method. On the other hand, bulky and costly instrument, tedious and prolonged procedures in traditional nucleic acid testing is also unfavorable for the screening. Delta is developing a fully automated hand-held POC using molecular technology to simultaneously detect 4 targets in 20 mins. The POC can be stand-alone or linked to mobile or PC app for different application scenarios. The proprietary recipe of the lysis buffer could neutralize the inhibition in the sample with effective pathogen lysis in seconds. The microfluidic cartridge containing lyophilized reagent can be stored at room temperature. The amplification signal is detected via the optical system from the colorimetric LAMP. This study is to evaluate the performance of the POC system in detecting SARS-CoV2 (SC2), FluA and B viruses in nasal swab. In addition, the assay performance was further compared with other commercial products available. Methods The attenuated SC2 virus, FluA and B viruses were diluted with 1xTE into different concentrations. The diluted viruses were spiked into the lysis buffer in 5% v/v ratio. Nasal samples collected from donors were soaked and swirled inside the cartridges containing lysis buffer according to the manufacturers’ instructions. For commercial products without lysis buffer, direct swabs were used. The cartridges were then put into the respective devices for further testing. Results The lyophilized SC2 assay consistently detected the viruses at 1000 copies/ml (20 out of 20) in real nasal samples in the presence of Delta Lysis Buffer (containing lyophilized neutralizer). However, without Lysis buffer, there was no signal detected even in the presence of 240x LoD viruses. The whole workflow sensitivity was tested with SC2 positive samples at 1x, 4x, 10x LoD and negative samples in the nasal sample matrix on Delta hand-held system. The results were processed via embedded algorithm and displayed on device as well as on PC and mobile app respectively. Out of 79 positive samples tested, the detection rate was 100%. There was no false positive detection for 31 negative samples tested. Positive internal control indicated the valid runs. 5 major SC2 subtypes (99.8% in silico analysis of 366,707 strains), 10 FluA and 9 FluB subtypes were successfully detected. Further, Delta hand-held platform outperformed in the LoD of SC2 with 500 copies/swab comparing to the ≥50,000 copies/swab of the benchmarked commercial platforms. Conclusions The performance of the hand-held molecular detection system we developed demonstrated a rapid, sensitive, and competitive analytical performance in the respiratory panel detection. The capability of device linking to apps and clouds for data management and the extendable docking system for multiple devices to run samples simultaneously and independently, make it applicable for the infectious diseases screening in both clinical settings and for home-use.

Frequent Evaluation of Herpes Simplex Virus Type 2 in Women with Genital Herpes by Realtime PCR

Background: Herpes simplex type 2 is a common infection worldwide. This disease is common in both developed and developing countries. Early detection of infection is very important to reduce the risk of infection. Real-time reliable PCR is a very sensitive and specific method that can be used as the best marker in determining the therapeutic effect by identifying a viral genome in an individual. The prevalence of herpes simplex virus type 2 in women with genital herpes was evaluated by Real time PCR method. Methods: From January 1999 to March 2010, 45 samples of vaginal swabs and cervix of women with genital herpes were examined for HSV virus DNA detection using Real Time PCR. Results: The mean age of the patients was 35.9 + 5.9. The percentage of positive cases of herpes simplex virus type 2 in the studied women was 22.2% and the history of infection with hpv was 33.3% vs. 12.5%. = 0.094 which was significant. Conclusion: Clinical specimens of vaginal swabs from genital herpes caused by herpes simplex virus 2 can be quantitatively analyzed instead of nucleic acid extraction and amplification by PCR.

DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.

Diabetic foot ulcers (DFUs) are a severe complication among diabetic patients, often leading to amputation or even death. Early detection of infection and ischemia is essential for improving healing outcomes, but current diagnostic methods are invasive, time-consuming, and costly. There is a need for non-invasive, efficient, and affordable solutions in diabetic foot care. We developed DFUCare, a platform that leverages computer vision and deep learning (DL) algorithms to localize, classify, and analyze DFUs non-invasively. The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. Additionally, deep-learning models were implemented to classify infection and ischemia in DFUs. The preliminary performance of the platform was tested on wound images acquired using a cell phone. DFUCare achieved an F1-score of 0.80 and a mean Average Precision (mAP) of 0.861 for wound localization. For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. The system successfully measured wound size and performed tissue color and textural analysis for a comparative assessment of macroscopic wound features. In clinical testing, DFUCare localized wounds and predicted infected and ischemic with an error rate of less than 10%, underscoring the strong performance of the platform. DFUCare presents an innovative approach to wound care, offering a cost-effective, remote, and convenient healthcare solution. By enabling non-invasive and accurate analysis of wounds using mobile devices, this platform has the potential to revolutionize diabetic foot care and improve clinical outcomes through early detection of infection and ischemia.

Interlaboratory Harmonization Study and Prospective Evaluation of the PURE–Trypanosoma cruzi–Loop-Mediated Isothermal Amplification Assay for Detecting Parasite DNA in Newborn's Dried Blood Spots

Timely diagnosis of vertical Trypanosoma cruzi infections involves microscopy-based detection of circulating parasites from peripheral blood, which lacks sensitivity and is operator dependent. Consequently, most children born to T. cruzi-infected mothers are required to undergo serological testing after nine months, which risks loss to follow-up. Alternatively, the Loop-mediated isothermal amplification (LAMP) test for T. cruzi DNA offers high analytical and clinical performance and easy to use in low-complexity laboratories. Recently, we optimized this technique using an ultra-rapid DNA extraction method combined with the LAMP in dried blood spots (DBS) on FTA cards. The procedure has been implemented in ten public maternities across Paraguay, Bolivia, and Argentina, involving the training of 14 technicians. Operators' performance was evaluated using a standardized DBS testing panel for harmonization, including negative controls and DBS samples artificially contaminated with T. cruzi at 50 and 20 cells/mL. There was strong agreement (ĸ = 0.924) for controls and 50 cells/mL samples, and good agreement (ĸ = 0.718) across all testing panels, even at the detection limit of the test. A prospective study collected 306 DBS from 222 newborns at birth and/or two months, detecting T. cruzi microscopically in four cases. LAMP identified eight positive cases and perfectly aligned with Real Time PCR (ĸ = 1), demonstrating higher sensitivity than microscopic observation for early detection of infection in infants.

Precision Medicine for Apical Lesions and Peri-Endo Combined Lesions Based on Transfer Learning Using Periapical Radiographs.

An apical lesion is caused by bacteria invading the tooth apex through caries. Periodontal disease is caused by plaque accumulation. Peri-endo combined lesions include both diseases and significantly affect dental prognosis. The lack of clear symptoms in the early stages of onset makes diagnosis challenging, and delayed treatment can lead to the spread of symptoms. Early infection detection is crucial for preventing complications. PAs used as the database were provided by Chang Gung Memorial Medical Center, Taoyuan, Taiwan, with permission from the Institutional Review Board (IRB): 02002030B0. The tooth apex image enhancement method is a new technology in PA detection. This image enhancement method is used with convolutional neural networks (CNN) to classify apical lesions, peri-endo combined lesions, and asymptomatic cases, and to compare with You Only Look Once-v8-Oriented Bounding Box (YOLOv8-OBB) disease detection results. The contributions lie in the utilization of database augmentation and adaptive histogram equalization on individual tooth images, achieving the highest comprehensive validation accuracy of 95.23% with the ConvNextv2 model. Furthermore, the CNN outperformed YOLOv8 in identifying apical lesions, achieving an F1-Score of 92.45%. For the classification of peri-endo combined lesions, CNN attained the highest F1-Score of 96.49%, whereas YOLOv8 scored 88.49%.

Wearable bracelet and machine learning for remote diagnosis and pandemic infection detection

The COVID-19 pandemic has highlighted that effective early infection detection methods are essential, as they play a critical role in controlling the epidemic spread. In this work, we investigate the use of wearable sensors in conjunction with machine learning (ML) techniques for pandemic infection detection. We work on designing a wristband that measures various vital parameters such as temperature, heart rate, and SPO2, and transmits them to a mobile application using Bluetooth Low Energy. The accuracy of the wristband measurements is shown to be within 10% of the readings of existing commercial products. The measured data can be used and analyzed for various purposes. To benefit from the existing online datasets related to COVID-19, we use this pandemic as an example in our work. Hence, we also develop ML-based models that use the measured vital parameters along with cough sounds in order to determine whether a case is COVID-19 positive or not. The proposed models are shown to achieve remarkable results, exceeding 90% accuracy. One of our proposed models exceeds 96% performance in terms of accuracy, precision, recall, and F1-Score. The system lends itself reasonably for amendment to deal with future pandemics by considering their specific features and designing the ML models accordingly. Furthermore, we design and develop a mobile application that shows the data collected from the wristband, records cough sounds, runs the ML model, and provides feedback to the user about their health status in a user-friendly, intuitive manner. A successful deployment of such an approach would decrease the load on hospitals and prevent infection from overcrowded spaces inside the hospital.

Mitigating infections in implantable urological continence devices: risks, challenges, solutions, and future innovations. A comprehensive literature review.

Stress urinary incontinence is a growing issue in ageing men, often following treatment for prostate cancer or bladder outflow obstruction. While implantable urological devices offer relief, infections are a significant concern. These infections can lead to device removal, negating the benefits and impacting patient outcomes. This review explores the risks and factors contributing to these infections and existing strategies to minimize them. These strategies encompass a multifaceted approach that considers patient-specific issues, environmental issues, device design and surgical techniques. However, despite these interventions, there is still a pressing need for further advancements in device infection prevention. Faster diagnostics, such as Raman spectroscopy, could enable early detection of infections. Additionally, biocompatible adjuncts like ultrasound-responsive microbubbles hold promise for enhanced drug delivery and biofilm disruption, particularly important as antibiotic resistance rises worldwide. By combining advancements in diagnostics, device design, and patient-specific surgical techniques, we can create a future where implantable urological devices offer men a significant improvement in quality of life with minimal infection risk.

Hospitalization Causes and Epidemiological Characteristics Among Patients With End-Stage Renal Disease: A Six-Year Retrospective Study.

Chronic kidney disease (CKD) leads to a high rate of complications requiring hospital admission for advanced management. Therefore, this study aims to analyze the main causes of hospitalization following the initiation of renal replacement therapy (RRT). This observational and descriptive study utilized a non-probabilistic quota sampling method, reviewing a total of 423 medical records from General Regional Hospital 1 of the Mexican Social Security Institute in Querétaro. The study evaluated the frequency and causality of hospitalizations during a retrospective period from 2018 to 2023. There were 1,162 hospitalization events involving 423 patients; 71.63% of patients started RRT with peritoneal dialysis, while 26% began with hemodialysis. The leading cause of hospitalization was electrolyte imbalance (397; 34.17%), followed by peritonitis associated with peritoneal dialysis (351; 30.21%), change to hemodialysis (270; 23.24%), Tenckhoff catheter dysfunction (209; 17.99%), and fluid overload (205; 17.64%). The group with the highest number of events was renal-related complications, followed by infectious causes. Hospitalizations in end-stage CKD patients often arise from the complex renal pathophysiology and complications related to acute and decompensated renal function. This condition refers to the kidneys' failure to maintain essential physiological functions despite ongoing treatment, leading to issues such as electrolyte imbalances, fluid overload, and uremic syndrome. To reduce morbidity and mortality, measures such as enhanced training in ambulatory dialysis, improved catheter care, and early infection detection are crucial. A comprehensive approach that addresses both acute issues and preventive strategies is essential for improving clinical outcomes and quality of life for these patients.

Exploring urinary proteomics and peptidomics biomarkers for the diagnosis of mekong schistosomiasis

Schistosomiasis caused by Schistosoma mekongi is one of the causative agents of human blood fluke infection in the lower Mekong River. Traditionally, the detection of egg morphology in stool samples has served as the prevailing method for diagnosing Schistosoma infection. Nonetheless, this approach exhibits low sensitivity, particularly in early infection detection. Urine has been extensively studied as a noninvasive clinical sample for diagnosing infectious diseases. Despite this, urine proteomic analysis of S. mekongi infection has been less investigated. This study aimed to characterize proteins and peptides present in mouse urine infected with S. mekongi both before infection and at intervals of 1, 2, 4, and 8 weeks post-infection using mass spectrometry-based proteomics. Proteomics analysis revealed 13 up- and only one down-regulated mouse protein consistently found across all time points. Additionally, two S. mekongi uncharacterized proteins were detected throughout the infection period. Using a peptidomics approach, we consistently identified two peptide sequences corresponding to S. mekongi collagen alpha-1(V) in mouse urine across all time points. These findings highlight the potential of these unique proteins, particularly the S. mekongi uncharacterized proteins and collagen alpha-1(V), as potential biomarkers for early detection of S. mekongi infection. Such insights could significantly advance diagnostic strategies for human Mekong schistosomiasis.

Rapid identification of early infections in febrile patients after CD19 target CAR-T cell therapy for B-cell malignancies

BackgroundCD19-targeted chimeric antigen receptor T (CAR-T) cell therapy stands out as a revolutionary intervention, exhibiting remarkable remission rates in patients with refractory/relapsed (R/R) B-cell malignancies. However, the potential side effects of therapy, particularly cytokine release syndrome (CRS) and infections, pose significant challenges due to their overlapping clinical features. Promptly distinguishing between CRS and infection post CD19 target CAR-T cell infusion (CTI) remains a clinical dilemma. Our study aimed to analyze the incidence of infections and identify key indicators for early infection detection in febrile patients within 30 days post-CTI for B-cell malignancies.MethodsIn this retrospective cohort study, a cohort of 104 consecutive patients with R/R B-cell malignancies who underwent CAR-T therapy was reviewed. Clinical data including age, gender, CRS, ICANS, treatment history, infection incidence, and treatment responses were collected. Serum biomarkers procalcitonin (PCT), interleukin-6 (IL-6), and C-reactive protein (CRP) levels were analyzed using chemiluminescent assays. Statistical analyses employed Pearson’s Chi-square test, t-test, Mann–Whitney U-test, Kaplan–Meier survival analysis, Cox proportional hazards regression model, Spearman rank correlation, and receiver operating characteristic (ROC) curve analysis to evaluate diagnostic accuracy and develop predictive models through multivariate logistic regression.ResultsIn this study, 38 patients (36.5%) experienced infections (30 bacterial, 5 fungal, and 3 viral) within the first 30 days of CAR T-cell infusion. In general, bacterial, fungal, and viral infections were detected at a median of 7, 8, and 9 days, respectively, after CAR T-cell infusion. Prior allogeneic hematopoietic cell transplantation (HCT) was an independent risk factor for infection (Hazard Ratio [HR]: 4.432 [1.262–15.565], P = 0.020). Furthermore, CRS was an independent risk factor for both infection ((HR: 2.903 [1.577–5.345], P < 0.001) and severe infection (9.040 [2.256–36.232], P < 0.001). Serum PCT, IL-6, and CRP were valuable in early infection prediction post-CAR-T therapy, particularly PCT with the highest area under the ROC curve (AUC) of 0.897. A diagnostic model incorporating PCT and CRP demonstrated an AUC of 0.903 with sensitivity and specificity above 83%. For severe infections, a model including CRS severity and PCT showed an exceptional AUC of 0.991 with perfect sensitivity and high specificity. Based on the aforementioned analysis, we proposed a workflow for the rapid identification of early infection during CAR-T cell therapy.ConclusionsCRS and prior allogeneic HCT are independent infection risk factors post-CTI in febrile B-cell malignancy patients. Our identification of novel models using PCT and CRP for predicting infection, and PCT and CRS for predicting severe infection, offers potential to guide therapeutic decisions and enhance the efficacy of CAR-T cell therapy in the future.

Dynamic pathology and pathogen distribution of the yeast Metschnikowia bicuspidata in the Chinese mitten crab (Eriocheir sinensis).

In this study, the "milky disease" model of Eriocheir sinhensis was constructed via intramuscular injection with the pathogenic yeast Metschnikowia bicuspidata. The dynamic pathological changes of E. sinensis after injection were elucidated with two staining methods (haemotoxylin-eosin and alcian blue periodic acid-Schiff) and fluorescence insitu hybridization technology. Anatomical observation revealed three stages of the "milky disease": no clinical signs (1-4 days after infection), the appearance of signs of disease (5-7 days), and significant liquefaction (10 days). Histological observation also revealed three stages of the disease: yeast diffusion (1-2 days after infection), yeast slow development (3-4 days), and yeast rapid proliferation (5 days). And FISH technique was suitable for the early detection of infection with M. bicuspidata in E. sinensis. We found that M. bicuspidata spread to the whole body of the crab through the haemolymph and developed into fungal septicaemia. These results elucidate the systemic pathological characteristics of "milky disease" in E. sinensis and suggest the pathogenic mechanism of M. bicuspidata.

Validation of a Clinical Scale for Early Detection of Infections at the Exit Site of Central Venous Catheters for Hemodialysis

Exit-site infections (ESI) of central venous catheters for hemodialysis (CVC-HD) has been associated with early catheter removal and an increased risk of CVC-HD related bacteremia. No specific clinical scales to predict ESI have previously been validated. A multicenter prospective cohort study was performed to validate the proposed scale, which is based on the following 5 signs and symptoms: (i) pain at exit site during interdialytic period; (ii) hyperemia or erythema≥2 cm from exit site; (iii) inflammation, induration, or swelling at exit site; (iv) fever≥38 °C not attributable to other causes, and (v) obvious abscess or purulent exudate at the exit site. Adult patients with a tunneled CVC-HD for at least 1 month after insertion has been included. During each hemodialysis session, the exit site was assessed with the proposed scale by nurses. If any item was present, a pericatheter skin swab culture was collected: positive results were gold standard. The scale was validated using receiver operating characteristic (ROC) curves and logistic regression analysis. For this purpose, the logit function was applied, and the ESI probability calculated, as elogit ESI/1+ elogit ESI. Three hundred thirty-seven CVC-HDs from 310 patients were analyzed, producing 515 cultures (117 infected and 398 healthy). The final version of the scale includes the following 3 signs and symptoms, which present the greatest predictive capacity: (i) pain at exit site during interdialytic period, (ii) hyperemia or erythema≥2 cm from exit site, and (iii) abscess or purulent exudate at the exit site. The final version generated an area under the ROC curve (AUC) of 88.3% (95% confidence interval [CI]: 85.2%-91%; P< 0.001), Youden index 0.7557 ≈ 1, sensitivity 80.34% (95% CI: 71.36%-87.71%) and specificity 95.23% (95% CI: 92.73%-97%). The validation shows that the scale has good predictive properties, detecting approximately 90% of ESI with very acceptable validity parameters.

Enhancements to the National HIV Surveillance System, United States, 2013-2023.

HIV infection is monitored through the National HIV Surveillance System (NHSS) to help improve the health of people with HIV and reduce transmission. NHSS data are routinely used at federal, state, and local levels to monitor the distribution and transmission of HIV, plan and evaluate prevention and care programs, allocate resources, inform policy development, and identify and respond to rapid transmission in the United States. We describe the expanded use of HIV surveillance data since the 2013 NHSS status update, during which time the Centers for Disease Control and Prevention (CDC) coordinated to revise the HIV surveillance case definition to support the detection of early infection and reporting of laboratory data, expanded data collection to include information on sexual orientation and gender identity, enhanced data deduplication processes to improve quality, and expanded reporting to include social determinants of health and health equity measures. CDC maximized the effects of federal funding by integrating funding for HIV prevention and surveillance into a single program; the integration of program funding has expanded the use of HIV surveillance data and strengthened surveillance, resulting in enhanced cluster response capacity and intensified data-to-care activities to ensure sustained viral suppression. NHSS data serve as the primary source for monitoring HIV trends and progress toward achieving national initiatives, including the US Department of Health and Human Services' Ending the HIV Epidemic in the United States initiative, the White House's National HIV/AIDS Strategy (2022-2025), and Healthy People 2030. The NHSS will continue to modernize, adapt, and broaden its scope as the need for high-quality HIV surveillance data remains.

Predicting Mortality for COVID-19 Patients Admitted to an Emergency Department Using Early Warning Scores in Poland.

COVID-19 disease is characterised by a wide range of symptoms that in most cases resemble flu or cold. Early detection of infections, monitoring of patients' conditions, and identification of patients with worsening symptoms became crucial during the peak of pandemic. The aim of this study was to assess and compare the performance of common early warning scores at the time of admission to an emergency department in predicting in-hospital mortality in patients with COVID-19. The study was based on a retrospective analysis of patients with SARS-CoV-2 infection admitted to an emergency department between March 2020 and April 2022. The prognostic value of early warning scores in predicting in-hospital mortality was assessed using the receiver operating characteristic (ROC) curve. Patients' median age was 59 years, and 52.33% were male. Among all the EWS we assessed, REMS had the highest overall accuracy (AUC 0.84 (0.83-0.85)) and the highest NPV (97.4%). REMS was the most accurate scoring system, characterised by the highest discriminative power and negative predictive value compared to the other analysed scoring systems. Incorporating these tools into clinical practice in a hospital emergency department could provide more effective assessment of mortality and, consequently, avoid delayed medical assistance.

Covid-19 Detection by Machine Learning Using Chest Radiographs

The recent pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has highlighted the importance of early detection of infections, especially when RT-PCR testing equipment is scarce. This study introduces a machine learning algorithm using CT scan imaging for rapid COVID-19 identification. The algorithm, designed as a computer-aided detection model, analyzed 536 CT images (32x32 pixels) categorized into COVID-19 infected and non-infected groups. The model preprocesses images using the Prewitt filter and discrete cosine transform, then extracts features through various statistical methods and the histogram of oriented gradients (HOG). Out of 32 analyzed features, 29 showed high significance (p-value &lt; 0.05), effectively distinguishing normal and abnormal cases. These features were classified using support vector machine (SVM) and k-nearest neighbor (KNN) methods. Performance metrics like sensitivity, specificity, and accuracy were used to evaluate the classifiers. The results of metrics showed that the classifiers of KNN-1, KNN-3, KNN-5, and SVM-Linear could distinguish between normal and abnormal images perfectly (100%) when it was applied to the proposed model on the tested ROIs images. Also, the SVM-RBF had less performance than other classifiers with 98.38% of accuracy but was still at a high-performance level. These results indicate that physicians can utilize the proposed model as an assisted tool for detecting COVID-19.

Experimental In Vitro Microfluidic Calorimetric Chip Data towards the Early Detection of Infection on Implant Surfaces.

Heat flux measurement shows potential for the early detection of infectious growth. Our research is motivated by the possibility of using heat flux sensors for the early detection of infection on aortic vascular grafts by measuring the onset of bacterial growth. Applying heat flux measurement as an infectious marker on implant surfaces is yet to be experimentally explored. We have previously shown the measurement of the exponential growth curve of a bacterial population in a thermally stabilized laboratory environment. In this work, we further explore the limits of the microcalorimetric measurements via heat flux sensors in a microfluidic chip in a thermally fluctuating environment.

Congenital CMV Infection: A Complex Case of Neurological Complications and Therapeutic Approaches in Infancy

Introduction and purpose&#x0D; Congenital Cytomegalovirus (CMV) infection stands as the prevalent infection among newborns and it may be associated with later complications such as progressive sensorineural hearing loss or neurological diseases. Infection occurs during pregnancy or during childbirth. Congenital CMV infections manifest in various ways, ranging from asymptomatic cases to severe complications such as microcephaly, hepatosplenomegaly, and chorioretinitis. Routine antibody screenings for pregnant women are not common, underlining the significance of early infection detection to minimize the risks of transmission to the fetus and potential complications in the child.&#x0D; The aim of this case report is to present the course of congenital CMV infection and its complication. This case report explores the challenges of managing an infant with congenital CMV infection complicated by hydrocephalus and viral co-infections. The infant underwent various treatments, including antiviral therapy, highlighting the need for multidisciplinary approaches.&#x0D; Conclusion&#x0D; With no available vaccine, prevention of congenital CMV infection relies on educating reproductive-age women and expectant mothers about infection risks. Early detection, frequent medical check-ups, and preventive education are crucial due to the absence of vaccines. Ongoing exploration of antiviral treatments and prompt interventions is imperative to improve outcomes for infants and families affected by the virus.

IPREDICT: AI enabled proactive pandemic prediction using biosensing wearable devices

The emergence of pandemics poses a persistent threat to both global health and economic stability. While zoonotic spillovers and local outbreaks may not be fully preventable, early detection of infections in individuals before they spread to communities can make a major difference in containing an infectious disease and stopping it from becoming an epidemic and then a pandemic.In this paper, we propose a novel Artificial Intelligence (AI)-based pandemic prediction framework called iPREDICT—a concept framework designed to leverage the power of AI and crowd-sensed data for accurate and timely pandemic prediction. The core idea of iPREDICT is to leverage the deluge of data that can be harnessed from connected and wearable biosensing devices. iPREDICT system then works by monitoring anomalies in the biomarkers at the individual level and correlating them with similar anomalies observed in other members of the community. Using AI-based anomaly detection in conjunction with analysis of the spatiotemporal growth of the correlated anomalies, iPREDICT thus can potentially detect and monitor the emergence of a local outbreak in near real-time to predict a potential pandemic.However, not every outbreak has the potential to become a pandemic. We illustrate how tools like graph neural networks can be leveraged to determine optimal thresholds as a function of a large number of demographical, social, and geographical factors that determine the spatiotemporal spread of an outbreak, thus quantifying the risk of it becoming an epidemic or pandemic.We also identify essential technological and social challenges that require attention to transform iPREDICT from an idea into a globally deployable solution for future pandemic prediction and management. To provide deeper insights into iPREDICT design challenges and trigger research towards possible solutions we present a COVID-19 based case study. The results signify the impact of variation in biosensing hardware, data sampling rate, and compression rate on the performance of AI models that underpin various components of the iPREDICT system.

Loop-Mediated Isothermal Amplification of DNA (LAMP) as an Alternative Method for Determining Bacteria in Wound Infections.

The increasing number of patients with chronic wounds requires the development of quick and accurate diagnostics methods. One of the key and challenging aspects of treating ulcers is to control wound infection. Early detection of infection is essential for the application of suitable treatment methods, such as systemic antibiotics or other antimicrobial agents. Clinically, the most frequently used method for detecting microorganisms in wounds is through a swab and culture on appropriate media. This test has major limitations, such as the long bacterial growth time and the selectivity of bacterial growth. This article presents an overview of molecular methods for detecting bacteria in wounds, including real-time polymerase chain reaction (rtPCR), quantitative polymerase chain reaction (qPCR), genotyping, next-generation sequencing (NGS), and loop-mediated isothermal amplification (LAMP). We focus on the LAMP method, which has not yet been widely used to detect bacteria in wounds, but it is an interesting alternative to conventional detection methods. LAMP does not require additional complicated equipment and provides the fastest detection time for microorganisms (approx. 30 min reaction). It also allows the use of many pairs of primers in one reaction and determination of up to 15 organisms in one sample. Isothermal amplification of DNA is currently the easiest and most economical method for microbial detection in wound infection. Direct visualization of the reaction with dyes, along with omitting DNA isolation, has increased the potential use of this method.

Monte Carlo simulations and phantom modeling for spatial frequency domain imaging of surgical wound monitoring.

Postoperative surgical wound infection is a serious problem around the globe, including in countries with advanced healthcare systems, and a method for early detection of infection is urgently required. We explore spatial frequency domain imaging (SFDI) for distinguishing changes in surgical wound healing based on the tissue scattering properties and surgical wound width measurements. A comprehensive numerical method is developed by applying a three-dimensional Monte Carlo simulation to a vertical heterogeneous wound model. The Monte Carlo simulation results are validated using resin phantom imaging experiments. We report on the SFDI lateral resolution with varying reduced scattering value and wound width and discuss the partial volume effect at the sharp vertical boundaries present in a surgical incision. The detection sensitivity of this method is dependent on spatial frequency, wound reduced scattering coefficient, and wound width. We provide guidelines for future SFDI instrument design and explanation for the expected error in SFDI measurements.