Assessment of using Google Trends for real-time monitoring of infectious disease outbreaks: a measles case study.

Measles remains a significant threat to children worldwide despite the availability of effective vaccines. The COVID-19 pandemic exacerbated the situation by leading to the postponement of supplementary measles immunization activities. Along with this postponement, measles surveillance also deteriorated, with the lowest number of submitted specimens in over a decade. In this study, we focus on measles as a challenging case study due to its high vaccination coverage, which leads to smaller outbreaks and potentially weaker signals on Google Trends. Our research aimed to explore the feasibility of using Google Trends for real-time monitoring of infectious disease outbreaks. We evaluated the correlation between Google Trends searches and clinical case data using the Pearson correlation coefficient and Spearman's rank correlation coefficient across 30 European countries and Japan. The results revealed that Google Trends was most suitable for monitoring acute disease outbreaks at the regional level in high-income countries, even when there are only a few weekly cases. For example, from 2017 to 2019, the Pearson correlation coefficient was 0.86 (p-value< 0.05) at the prefecture level for Okinawa, Japan, versus 0.33 (p-value< 0.05) at the national level for Japan. Furthermore, we found that the Pearson correlation coefficient may be more suitable than Spearman's rank correlation coefficient for evaluating the correlations between Google Trends search data and clinical case data. This study highlighted the potential of utilizing Google Trends as a valuable tool for timely public health interventions to respond to infectious disease outbreaks, even in the context of diseases with high vaccine coverage.

Comparison of a static cohort model and dynamic transmission model for respiratory syncytial virus intervention programs for infants in England and Wales.

BACKGROUND: A recent study comparing results of multiple cost-effectiveness analyses (CEAs) in a hypothetical population found that monoclonal antibody (mAb) immunoprophylaxis for respiratory syncytial virus (RSV) in infants averted fewer medically attended cases when estimated using dynamic transmission models (DTMs) versus static cohort models (SCMs). We aimed to investigate whether model calibration or parameterization could be the primary driver of inconsistencies between SCM and DTM predictions. METHODS: A recently published DTM evaluating the CEA of infant mAb immunoprophylaxis in England and Wales (EW) was selected as the reference model. We adapted our previously published SCM for US infants to EW by utilizing the same data sources used by the DTM. Both models parameterized mAb efficacy from a randomized clinical trial (RCT) that estimated an average efficacy of 74.5% against all medically attended RSV episodes and 62.1% against RSV hospitalizations. To align model assumptions, we modified the SCM to incorporate waning efficacy. Since the estimated indirect effects from the DTM were small (i.e., approximately 100-fold smaller in magnitude than direct effects), we hypothesized that alignment of model parameters should result in alignment of model predictions. Outputs for model comparison comprised averted hospitalizations and averted GP visits, estimated for seasonal (S) and seasonal-with-catchup (SC) immunization strategies. RESULTS: When we aligned the SCM intervention parameters to DTM intervention parameters, significantly more averted hospitalizations were predicted by the SCM (S: 32.3%; SC: 51.3%) than the DTM (S: 17.8%; SC: 28.6%). The SCM most closely replicated the DTM results when the initial efficacy of the mAb intervention was 62.1%, leading to an average efficacy of 39.3%. Under this parameterization the SCM predicted 17.4% (S) and 27.7% (SC) averted hospitalizations. Results were similar for averted GP visits. CONCLUSIONS: Parameterization of the RSV mAb intervention efficacy is a plausible primary driver of differences between SCM versus DTM model predictions.

Africa and climate justice at COP27 and beyond: impacts and solutions through an interdisciplinary lens.

Climate justice is not just a financial transaction to protect the environment. It needs to be seen as the protection of the most vulnerable in society after centuries of resource exploitation. African countries disproportionately face impacts of climate change on their environments, their economies, their resources and their infrastructure. This leads to greater vulnerability and increased exposure to the negative effects of a changing climate. In this article, we highlight the importance of climate justice and its role within the United Nations negotiations, and ultimately in concrete action. We discuss current climate impacts across key sectors in the African region, with a focus on health, infrastructure, food and water scarcity, energy and finance. All sectors are affected by climate change. They are interconnected and under threat. This triggers a ripple effect, where threats in one sector have a knock-on effect on other sectors. We find that the current set of intergovernmental institutions have failed to adequately address climate justice. We also contend that a siloed approach to climate action has proven to be ineffective. As we head towards the next set of negotiations (COP27), this paper argues that the economic and social conditions in Africa can be addressed through financial and collaborative support for adaptation and localised solutions, but that this will only be achieved if climate justice is prioritised by the decision makers. This needs to include a global-scale transition in how climate finance is assessed and accessed. Climate justice underpins real, effective and sustainable solutions for climate action in Africa.

A short history of the successes and failures of the international climate change negotiations.

The last 35 years have been a period of intense and continuous international negotiations to deal with climate change. During the same period of time humanity has doubled the amount of anthropogenic carbon dioxide in the atmosphere. There has, however, been progress and some notable successes in the negotiations. In 2015, at COP21 of the United Nations Framework Convention on Climate Change, 196 countries adopted the Paris Agreement stating that they would limit global temperatures to well below 2°C above pre-industrial levels and would pursue efforts to limit the temperature increase to 1.5°C above pre-industrial levels. The first review of the Paris Agreement was at COP26 in Glasgow with many countries pledging to go to net zero emissions by the middle of the century. But currently these pledges, if fulfilled, will only limit the global average temperature to between 2.4°C and 2.8°C. At COP27 in Egypt the core agreements from the Glasgow Climate Pact were maintained and countries finally agreed to set up a loss and damage facility - although details of who will provide the finance and who can claim are still be to be worked out. This article reviews the key moments in the history of international climate change negotiations and discusses what the key objectives are for future COP meetings.

Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case Study.

The COVID-19 pandemic has disrupted the seasonal patterns of several infectious diseases. Understanding when and where an outbreak may occur is vital for public health planning and response. We usually rely on well-functioning surveillance systems to monitor epidemic outbreaks. However, not all countries have a well-functioning surveillance system in place, or at least not for the pathogen in question. We utilized Google Trends search results for RSV-related keywords to identify outbreaks. We evaluated the strength of the Pearson correlation coefficient between clinical surveillance data and online search data and applied the Moving Epidemic Method (MEM) to identify country-specific epidemic thresholds. Additionally, we established pseudo-RSV surveillance systems, enabling internal stakeholders to obtain insights on the speed and risk of any emerging RSV outbreaks in countries with imprecise disease surveillance systems but with Google Trends data. Strong correlations between RSV clinical surveillance data and Google Trends search results from several countries were observed. In monitoring an upcoming RSV outbreak with MEM, data collected from both systems yielded similar estimates of country-specific epidemic thresholds, starting time, and duration. We demonstrate in this study the potential of monitoring disease outbreaks in real time and complement classical disease surveillance systems by leveraging online search data.

Chondrosarcoma of the thyroid cartilage: surgical management of a rare case.

Laryngeal malignancy encompasses about 1% of all cancers. Chondrosarcoma in the head and neck region represents about 0.1% of head and neck malignancies. Typical presenting symptoms relate to the anatomical location of these tumours and include dysphonia, inspiratory stridor, dysphagia, odynophagia or a neck mass. Benign and malignant cartilaginous cancers of the larynx have been described, and preoperative diagnosis can be difficult. Our report highlights the surgical management of a male patient in his 50s with chondrosarcoma of the thyroid cartilage.

Clinical decision making in paediatric otitis media: A pilot quality improvement study.

OBJECTIVES: This study aimed to improve local management of paediatric otitis media using the 2022 American-Academy of Otolaryngology Head and Neck Surgery (AAO-HNS) Clinical Practice Guideline for Tympanostomy Tubes in Children. METHODS: A SQUIRE 2.0 compliant quality improvement study was carried out within the outpatient department of our otolaryngology tertiary referral centre. Local outpatient management of paediatric otitis media was compared to the AAO-HNS guidelines, specifically focussing on the decision to offer tympanostomy tubes. Following initial audit of practice, an educational session was performed within the otolaryngology department and local practice was subsequently re-evaluated. RESULTS: Overall, 88 children were included (46 pre-intervention and 42 post-intervention) with a mean age of 7.6 years (range 1-12 years). Sixty-four (72.7%) children presented with suspected otitis media with effusion. The remainder presented with recurrent or persistent acute otitis media (24/88, 27.3%). Twenty-six children were offered tympanostomy tubes (29.5%). Initial evaluation of practice identified that the decision to offer tympanostomy tubes was guideline appropriate in 76.1% of children (35/46). This significantly improved following an educational session (40/42, 95.2%, p = 0.02). The reasons for non-guideline compliance included: otitis media with effusion <3 months, no evidence of middle ear fluid and patients meeting criteria for tympanostomy tube insertion not being offered. CONCLUSIONS: A focussed educational intervention may improve local adherence to guidelines in the management of paediatric otitis media. Continued re-evaluation of local practice is essential in order to ensure children are managed in accordance with the guidelines.

The clinical impact of multiple prevention strategies for respiratory syncytial virus infections in infants and high-risk toddlers in the United States.

BACKGROUND: Respiratory syncytial virus (RSV) remains a leading cause of medically-attended acute respiratory infection in infants and children. With multiple preventative interventions under development, accurate estimates of health care resource utilization are essential for policy decision making. METHODS: We developed a literature-based decision-tree model that estimated annual medically-attended RSV (MA-RSV) lower respiratory tract infection (LRTI) and non-LRTI episodes in the US for all infants and for high-risk toddlers. The model accounted for the gestational age and birth-month of infants, and the seasonal variation in RSV incidence. The impact of no prophylaxis, palivizumab, maternal vaccine, and long-acting monoclonal antibody (mAb) interventions was estimated. RESULTS: We estimated 1.23 million (range: 0.96 million-1.40 million) annual MA-RSV LRTI/non-LRTI episodes comprised of 1.19 million (range: 0.93 million-1.36 million) emergency department (ED) and outpatient visits, and 39,040 (range: 32,726-45,851) hospitalizations. Outpatient and ED visits were comprised of 586,034 (range: 430,595-718,868) LRTIs and 608,733 (range: 495,705-644,658) non-LRTIs. The long-acting mAb intervention resulted in the greatest number of averted outpatient and ED episodes (310,997 [53%] LRTIs; 284,305 [47%] non-LRTIs) and hospitalizations (21,845 [56%]). Full-term infants constitute the highest proportion of episodes across all interventions. CONCLUSIONS: MA-RSV disease is substantial in infants and high-risk toddlers. Long-acting mAbs are most effective at reducing the number of MA-RSV LRTI/non-LRTI episodes, and the only intervention that prevents disease in older infants (≥6 months old).

Foreign accent syndrome post tonsillectomy: A case report.

Foreign Accent Syndrome (FAS) is a rare clinical entity in which affected patients experience a new pattern of speech resembling an unusual accent. Reported cases are scarce in published literature and are usually the result of a neurological insult. FAS as a complication from a general anesthetic or surgery has not been reported to date. We present the case of a healthy 27-year-old Australian woman who developed FAS following a tonsillectomy. Post operatively, speech patterns resembled an Irish accent. We discuss the potential mechanisms of the unusual complication as well as review the available literature surrounding FAS.

Use of mathematical modelling to assess respiratory syncytial virus epidemiology and interventions: a literature review.

Respiratory syncytial virus (RSV) is a leading cause of acute lower respiratory tract infection worldwide, resulting in approximately sixty thousand annual hospitalizations of< 5-year-olds in the United States alone and three million annual hospitalizations globally. The development of over 40 vaccines and immunoprophylactic interventions targeting RSV has the potential to significantly reduce the disease burden from RSV infection in the near future. In the context of RSV, a highly contagious pathogen, dynamic transmission models (DTMs) are valuable tools in the evaluation and comparison of the effectiveness of different interventions. This review, the first of its kind for RSV DTMs, provides a valuable foundation for future modelling efforts and highlights important gaps in our understanding of RSV epidemics. Specifically, we have searched the literature using Web of Science, Scopus, Embase, and PubMed to identify all published manuscripts reporting the development of DTMs focused on the population transmission of RSV. We reviewed the resulting studies and summarized the structure, parameterization, and results of the models developed therein. We anticipate that future RSV DTMs, combined with cost-effectiveness evaluations, will play a significant role in shaping decision making in the development and implementation of intervention programs.

Differential hysteresis scanning of non-templated monomodal amorphous aerogels.

We perform Differential Hysteresis Scanning (DHS) Porosimetry of amorphous silicon oxycarbide aerogels to quantify hierarchical connectivity in these porous materials. We contrast high-resolution argon sorption scanning isotherms of samples obtained through a non-templated synthesis using different solvents, and characterize respective changes after calcination at 1000 °C. The multi-scan DHS data sets are analyzed through non-negative least-squares deconvolution using a kernel of theoretically derived isotherms for a selection of hierarchical geometries using non-local density functional theory (NL-DFT). We obtain two-dimensional contour plots that characterize mesopores according to the ratio between pore diameter and its connecting window. Combined information from DHS and complementary BET and BJH approaches reveals one system with monomodal distribution both in pore diameters and in window diameters. Hence, this amorphous material exhibits a uniformity usually only observed for crystalline systems. We demonstrate that DHS analysis provides quantitative data analyzing the hierarchical structure of mesoporous materials and unlocks pathways towards tailored materials with control of surface heterogeneity, localization, and sequential accessibility - even for amorphous systems.

Mixed infected laryngocoele presenting as airway obstruction: a case report.

Laryngocoele is a rare entity, defined as an abnormal cystic dilatation of saccule of the laryngeal ventricle. Three types of laryngocele have been described, based on their relation to the thyrohyoid membrane: internal, external or mixed type. Symptoms are variable, including neck swelling, shortness of breath, dysphonia and fever, if the laryngocoele becomes infected. Patients may also present in extremis with airway obstruction. We present the case of a healthy 34-year-old gentleman with acute airway obstruction due to a mixed infected laryngocoele. Flexible nasoendoscopy showed a large cystic swelling arising from the laryngeal ventricle. Computed tomography of neck confirmed a right paraglottic collection extending into the ventricle and glottis, causing significant airway compromise. The patient was managed with microlaryngoscopy and cystic decompression. At outpatient follow up, he was completely asymptomatic and is currently under surveillance. Endoscopic decompression is a safe and effective initial management for mixed laryngocoele.

Combined Tumor Environment Triggered Self-Assembling Peptide Nanofibers and Inducible Multivalent Ligand Display for Cancer Cell Targeting with Enhanced Sensitivity and Specificity.

Many new technologies, such as cancer microenvironment-induced nanoparticle targeting and multivalent ligand approach for cell surface receptors, are developed for active targeting in cancer therapy. While the principle of each technology is well illustrated, most systems suffer from low targeting specificity and sensitivity. To fill the gap, this work demonstrates a successful attempt to combine both technologies to simultaneously improve cancer cell targeting sensitivity and specificity. Specifically, the main component is a targeting ligand conjugated self-assembling monomer precursor (SAM-P), which, at the tumor site, undergoes tumor-triggered cleavage to release the active form of self-assembling monomer capable of forming supramolecular nanostructures. Biophysical characterization confirms the chemical and physical transformation of SAM-P from unimers or oligomers with low ligand valency to supramolecular assemblies with high ligand valency under a tumor-mimicking reductive microenvironment. The in vitro fluorescence assay shows the importance of supramolecular morphology in mediating ligand-receptor interactions and targeting sensitivity. Enhanced targeting specificity and sensitivity can be achieved via tumor-triggered supramolecular assembly and induces multivalent ligand presentation toward cell surface receptors, respectively. The results support this combined tumor microenvironment-induced cell targeting and multivalent ligand display approach, and have great potential for use as cell-specific molecular imaging and therapeutic agents with high sensitivity and specificity.

Machine learning approach to predict postoperative opioid requirements in ambulatory surgery patients.

Opioids play a critical role in acute postoperative pain management. Our objective was to develop machine learning models to predict postoperative opioid requirements in patients undergoing ambulatory surgery. To develop the models, we used a perioperative dataset of 13,700 patients (≥ 18 years) undergoing ambulatory surgery between the years 2016-2018. The data, comprising of patient, procedure and provider factors that could influence postoperative pain and opioid requirements, was randomly split into training (80%) and validation (20%) datasets. Machine learning models of different classes were developed to predict categorized levels of postoperative opioid requirements using the training dataset and then evaluated on the validation dataset. Prediction accuracy was used to differentiate model performances. The five types of models that were developed returned the following accuracies at two different stages of surgery: 1) Prior to surgery-Multinomial Logistic Regression: 71%, Naïve Bayes: 67%, Neural Network: 30%, Random Forest: 72%, Extreme Gradient Boost: 71% and 2) End of surgery-Multinomial Logistic Regression: 71%, Naïve Bayes: 63%, Neural Network: 32%, Random Forest: 72%, Extreme Gradient Boost: 70%. Analyzing the sensitivities of the best performing Random Forest model showed that the lower opioid requirements are predicted with better accuracy (89%) as compared with higher opioid requirements (43%). Feature importance (% relative importance) of model predictions showed that the type of procedure (15.4%), medical history (12.9%) and procedure duration (12.0%) were the top three features contributing to model predictions. Overall, the contribution of patient and procedure features towards model predictions were 65% and 35% respectively. Machine learning models could be used to predict postoperative opioid requirements in ambulatory surgery patients and could potentially assist in better management of their postoperative acute pain.

Forecasting a Crisis: Machine-Learning Models Predict Occurrence of Intraoperative Bradycardia Associated With Hypotension.

BACKGROUND: Predictive analytics systems may improve perioperative care by enhancing preparation for, recognition of, and response to high-risk clinical events. Bradycardia is a fairly common and unpredictable clinical event with many causes; it may be benign or become associated with hypotension requiring aggressive treatment. Our aim was to build models to predict the occurrence of clinically significant intraoperative bradycardia at 3 time points during an operative course by utilizing available preoperative electronic medical record and intraoperative anesthesia information management system data. METHODS: The analyzed data include 62,182 scheduled noncardiac procedures performed at the University of Washington Medical Center between 2012 and 2017. The clinical event was defined as severe bradycardia (heart rate <50 beats per minute) followed by hypotension (mean arterial pressure <55 mm Hg) within a 10-minute window. We developed models to predict the presence of at least 1 event following 3 time points: induction of anesthesia (TP1), start of the procedure (TP2), and 30 minutes after the start of the procedure (TP3). Predictor variables were based on data available before each time point and included preoperative patient and procedure data (TP1), followed by intraoperative minute-to-minute patient monitor, ventilator, intravenous fluid, infusion, and bolus medication data (TP2 and TP3). Machine-learning and logistic regression models were developed, and their predictive abilities were evaluated using the area under the ROC curve (AUC). The contribution of the input variables to the models were evaluated. RESULTS: The number of events was 3498 (5.6%) after TP1, 2404 (3.9%) after TP2, and 1066 (1.7%) after TP3. Heart rate was the strongest predictor for events after TP1. Occurrence of a previous event, mean heart rate, and mean pulse rates before TP2 were the strongest predictor for events after TP2. Occurrence of a previous event, mean heart rate, mean pulse rates before TP2 (and their interaction), and 15-minute slopes in heart rate and blood pressure before TP2 were the strongest predictors for events after TP3. The best performing machine-learning models including all cases produced an AUC of 0.81 (TP1), 0.87 (TP2), and 0.89 (TP3) with positive predictive values of 0.30, 0.29, and 0.15 at 95% specificity, respectively. CONCLUSIONS: We developed models to predict unstable bradycardia leveraging preoperative and real-time intraoperative data. Our study demonstrates how predictive models may be utilized to predict clinical events across multiple time intervals, with a future goal of developing real-time, intraoperative, decision support.

Immune globulin subcutaneous, human 20% solution (Xembify®), a new high concentration immunoglobulin product for subcutaneous administration.

Immune globulin subcutaneous, human 20% solution (IGSC-C 20%, Xembify®)-a new 20% immunoglobulin (IgG) liquid product for subcutaneous (SC) administration-has been developed by Grifols. The IGSC-C 20% formulation is based on knowledge acquired from the formulation of Immune Globulin Injection (Human),10% Caprylate/Chromatography Purified (IGIV-C 10%, Gamunex®-C). The protein concentration was increased from 10% to 20% to provide a smaller volume for SC administration. The IGSC-C 20% manufacturing process employs the same caprylate/chromatography purification steps as IGIV-C 10%, with the addition of an ultrafiltration step so that the product can be formulated at a higher protein concentration. IGSC-C 20% has been produced at full industrial scale to support clinical studies and licensure. These batches were characterized using a comprehensive panel of analytical testing. The new IGSC-C 20% product maintains the same composition, neutralizing activity, purity, and quality characteristics found in IGIV-C 10%.

Aviation-Style Computerized Surgical Safety Checklist Displayed on a Large Screen and Operated by the Anesthesia Provider Improves Checklist Performance.

BACKGROUND: Many hospitals have implemented surgical safety checklists based on the World Health Organization surgical safety checklist, which was associated with improved outcomes. However, the execution of the checklists is frequently incomplete. We reasoned that aviation-style computerized checklist displayed onto large, centrally located screen and operated by the anesthesia provider would improve the performance of surgical safety checklist. METHODS: We performed a prospective before and after observational study to evaluate the effect of a computerized surgical safety checklist system on checklist performance. We created checklist software and translated our 4-part surgical safety checklist from wall poster into an aviation-style computerized format displayed onto a large, centrally located screen and operated by the anesthesia provider. Direct observers recorded performance of the first part of the surgical safety checklist that was initiated before anesthetic induction, including completion of each checklist item, provider participation and distraction level, resistance to use of the checklist, and the time required for checklist completion before and after checklist system implementation. We compared trends of the proportions of cases with 100% surgical safety checklist completion over time between pre- and postintervention periods and assessed for a jump at the start of intervention using segmented logistic regression model while controlling for potential confounding variables. RESULTS: A total of 671 cases were observed before and 547 cases were observed after implementation of the computerized surgical safety checklist system. The proportion of cases in which all of the items of the surgical safety checklist were completed significantly increased from 2.1% to 86.3% after the computerized checklist system implementation (P < .001). Before computerized checklist system implementation, 488 of 671 (72.7%) cases had <75% of checklist items completed, whereas after a computerized checklist system implementation, only 3 of 547 (0.5%) cases had <75% of checklist items completed. CONCLUSIONS: The implementation of a computerized surgical safety checklist system resulted in an improvement in checklist performance.

Comparison of the liquid and lyophilized formulations of Prolastin®-C for Alpha1-Antitrypsin deficiency: Biochemical characteristics, pharmacokinetics, safety and neoantigenicity in rabbits.

Multiple analytical and preclinical studies were performed to compare the biochemical characteristics, pharmacokinetics (PK), safety and neoantigenicity of a new 5% liquid formulation of Alpha-1 Proteinase Inhibitor (Liquid A1PI, Prolastin®-C Liquid) with the lyophilized version (Lyophilized A1PI, Prolastin®-C). Liquid A1PI and Lyophilized A1PI had similar average mass (~52 kDa), and both forms exhibited glycoform patterns consistent with the known banding pattern of A1PI (dominated by the M6 and M4 bands, including deconvoluted masses). Both Liquid A1PI and Lyophilized A1PI yielded average percent purity values ranging from 96% to 99% and had active content ranging from 53  mg/mL to 59  mg/mL. The PK profile of Liquid A1PI was similar to Lyophilized A1PI. Safety assessments in rabbits showed good tolerability and no test article-related changes in mortality, clinical signs, clinical pathology, body weight, food consumption, or urinalysis parameters. Following immunodepletion of antibodies that recognize Lyophilized A1PI, there were no significant differences in the anti-drug titers among animals immunized with Lyophilized A1PI and Liquid A1PI (p > 0.05), indicating that no antibodies to neoantigens were generated. Liquid A1PI and Lyophilized A1PI have similar profiles with respect to biochemical characteristics, PK, safety and neoantigenicity.

Partnership with Interventional Pulmonologist: An Anesthesiologist's Perspective.

Via the emergence of new bronchoscopic technologies and techniques, there is enormous growth in the number of procedures being performed in nonoperating room settings. This, coupled with a greater focus from the Centers for Medicare and Medicaid Services for mandated anesthesiology oversight of procedural sedation for bronchoscopy by the pulmonologists has led to a more frequent working partnership between interventional pulmonologists and anesthesiologists. This article offers the interventional pulmonologist insight into how the anesthesiologist thinks and approaches anesthetic care delivery.