VISUAL AUDIO MESSAGES

Computing devices (e.g., a cellular phone, a smartphone, a desktop computer, a laptop computer, a tablet computer, a portable gaming device, a watch, etc.). may enable users to exchange electronic communication including both a recorded message, such as an audio recording, a video recording, etc., as well as a transcript of the recorded message. In some examples, a first computing device may record audio from a first user and perform speech-to-text to generate a transcript of the recorded audio. The first computing device may then send the recorded message and the transcript of the recorded message in a single electronic communication to a second computing device (e.g., being used by a second user). Because the electronic communication includes the recorded message and the transcript of the recorded message, the second user can both listen to and read the recorded message, which may improve consumption of the recorded message (e.g., because background noise may make listening to the recorded message difficult, reading a transcript of the recorded message may be faster than listening to the recorded message, etc.). To facilitate a hands-free user experience, the computing device may include a voice user interface (VUI) by which a user may compose the electronic communication. For example, the user may provide voice commands (e.g., “clear”, “send”, “browse”, etc.) to cause the computing device to perform corresponding functions with respect to the electronic communication. Furthermore, the computing device may provide one or more instructions for using voice commands. In some cases, the instructions may relate to the action currently being taken by the user, a context of the electronic communication, etc

Inline Text Entry On Portable Electronic Devices

This publication describes systems and techniques to provide inline text entry on portable electronic devices. Portable electronic devices, such as smartphones, generally include an on-screen keyboard to allow users to input alphanumeric characters. These keyboards generally provide several suggestions of the word that the user is currently typing or the next word to be input. Because the keyboard has a limited area on the graphical user interface (GUI) to display candidate words, the keyboard can only present a few suggestions (e.g., two or three candidates), which are generally single-word candidates. This publication describes a keyboard for portable electronic devices that displays inline candidate words, which can include multiple words, entire phrases, and complete sentences. The inline suggestions can be shown directly in the editor box of an application or a pop-up window. The inline suggestions allow users to type faster and reduce spelling and grammatical errors in applications on portable electronic devices

VIRTUAL KEYBOARD WITH INTEGRATED SUGGESTION FEATURES

A computing device may present a virtual keyboard with integrated suggestion features that improve the speed and efficiency of correcting typographical errors (e.g., spelling and/or grammar errors) or text otherwise warranting correction. The virtual keyboard may be configured to present one or more suggestions for correcting typographical errors (also referred to herein as “typos”) identified by the computing device. The computing device may display a virtual keyboard graphical user interface (GUI) that includes one or more suggestions for correcting each typo in a suggestion strip GUI. The suggestion strip GUI may be a contiguous region in line with and/or directly above the virtual keyboard rather than within a graphical element that overlays a portion of the virtual keyboard GUI and visually obscures the virtual keyboard GUI. In some instances, the suggested correction or an explanation of the error may be included within the virtual keyboard GUI in place of the keyboard itself or a combination thereof (e.g., a suggested correction within the suggestion strip GUI and an explanation of the error in place of the virtual keyboard GUI)

Cohort for Tuberculosis Research by the Indo-US Medical Partnership (CTRIUMPH): protocol for a multicentric prospective observational study

INTRODUCTION: Tuberculosis disease (TB) remains an important global health threat. An evidence-based response, tailored to local disease epidemiology in high-burden countries, is key to controlling the global TB epidemic. Reliable surrogate biomarkers that predict key active disease and latent TB infection outcomes are vital to advancing clinical research necessary to ‘End TB’. Well executed longitudinal studies strengthening local research capacity for addressing TB research priorities and advancing biomarker discovery are urgently needed. METHODS AND ANALYSIS: The Cohort for Tuberculosis Research by the Indo-US Medical Partnership (CTRIUMPH) study conducted in Byramjee Jeejeebhoy Government Medical College (BJGMC), Pune and National Institute for Research in Tuberculosis (NIRT), Chennai, India, will establish and maintain three prospective cohorts: (1) an Active TB Cohort comprising 800 adults with pulmonary TB, 200 adults with extrapulmonary TB and 200 children with TB; (2) a Household Contact Cohort of 3200 adults and children at risk of developing active disease; and (3) a Control Cohort consisting of 300 adults and 200 children with no known exposure to TB. Relevant clinical, sociodemographic and psychosocial data will be collected and a strategic specimen repository established at multiple time points over 24 months of follow-up to measure host and microbial factors associated with (1) TB treatment outcomes; (2) progression from infection to active TB disease; and (3) Mycobacterium tuberculosis transmission among Indian adults and children. We anticipate CTRIUMPH to serve as a research platform necessary to characterise some relevant aspects of the TB epidemic in India, generate evidence to inform local and global TB control strategies and support novel TB biomarker discovery. ETHICS AND DISSEMINATION: This study is approved by the Institutional Review Boards of NIRT, BJGMC and Johns Hopkins University, USA. Study results will be disseminated through peer-reviewed journals and research conferences. FUNDING: NIH/DBT Indo-US Vaccine Action Programme and the Indian Council of Medical Research

Smoking, alcohol use disorder and tuberculosis treatment outcomes: A dual co-morbidity burden that cannot be ignored

BackgroundMore than 20% of tuberculosis (TB) disease worldwide may be attributable to smoking and alcohol abuse. India is the second largest consumer of tobacco products, a major consumer of alcohol particularly among males, and has the highest burden of TB globally. The impact of increasing tobacco dose, relevance of alcohol misuse and past versus current or never smoking status on TB treatment outcomes remain inadequately defined.MethodsWe conducted a multi-centric prospective cohort study of newly diagnosed adult pulmonary TB patients initiated on TB treatment and followed for a minimum of 6 months to assess the impact of smoking status with or without alcohol abuse on treatment outcomes. Smokers were defined as never smokers, past smokers or current smokers. Alcohol Use Disorder Identification Test (AUDIT) scores were used to assess alcohol misuse. The association between smoking status and treatment outcomes was assessed in univariate and multivariate random effects poisson regression models.ResultsOf 455 enrolled, 129 (28%) had a history of smoking with 94 (20%) current smokers and 35 (8%) past smokers. Unfavourable treatment outcomes were significantly higher among past and current smokers as compared to never smokers. Specifically, the risk of treatment failure was significantly higher among past smokers (aIRR = 2.66, 95% CI: 1.41-4.90, p = 0.002), recurrent TB among current smokers (aIRR = 2.94, 95% CI: 1.30-6.67, p = 0.010) and death among both past (2.63, 95% CI: 1.11-6.24, p = 0.028) and current (aIRR = 2.59, 95% CI: 1.29-5.18, p = 0.007) smokers. Furthermore, the combined effect of alcohol misuse and smoking on unfavorable treatment outcomes was significantly higher among past smokers (aIRR: 4.67, 95% CI: 2.17-10.02, pConclusionPast and current smoking along with alcohol misuse have combined effects on increasing the risk of unfavourable TB treatment outcomes. Innovative interventions that can readily address both co-morbidities are urgently needed

Host lipidome and tuberculosis treatment failure

INTRODUCTION: Host lipids play important roles in tuberculosis (TB) pathogenesis. Whether host lipids at TB treatment initiation (baseline) affect subsequent treatment outcomes has not been well characterised. We used unbiased lipidomics to study the prospective association of host lipids with TB treatment failure. METHODS: A case–control study (n=192), nested within a prospective cohort study, was used to investigate the association of baseline plasma lipids with TB treatment failure among adults with pulmonary TB. Cases (n=46) were defined as TB treatment failure, while controls (n=146) were those without failure. Complex lipids and inflammatory lipid mediators were measured using liquid chromatography mass spectrometry techniques. Adjusted least-square regression was used to assess differences in groups. In addition, machine learning identified lipids with highest area under the curve (AUC) to classify cases and controls. RESULTS: Baseline levels of 32 lipids differed between controls and those with treatment failure after false discovery rate adjustment. Treatment failure was associated with lower baseline levels of cholesteryl esters and oxylipin, and higher baseline levels of ceramides and triglycerides compared to controls. Two cholesteryl ester lipids combined in a unique classifier model provided an AUC of 0.79 (95% CI 0.65–0.93) in the test dataset for prediction of TB treatment failure. CONCLUSIONS: We identified lipids, some with known roles in TB pathogenesis, associated with TB treatment failure. In addition, a lipid signature with prognostic accuracy for TB treatment failure was identified. These lipids could be potential targets for risk-stratification, adjunct therapy and treatment monitoring

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation