Economic cost of tobacco use in India, 2004

ObjectiveTo estimate the tobacco-attributable costs of diseases separately for smoked and smokeless tobacco use in India.MethodsThe prevalence-based attributable-risk approach was used to estimate the economic cost of tobacco using healthcare expenditure data from the National Sample Survey, a nationally representative household sample survey conducted in India in 2004. Four major categories of tobacco-related disease-tuberculosis, respiratory diseases, cardiovascular diseases and neoplasms-were considered.ResultsDirect medical costs of treating tobacco related diseases in India amounted to $907 million for smoked tobacco and$285 million for smokeless tobacco. The indirect morbidity costs of tobacco use, which includes the cost of caregivers and value of work loss due to illness, amounted to $398 million for smoked tobacco and$104 million for smokeless tobacco. The total economic cost of tobacco use amounted to $1.7 billion. Tuberculosis accounted for 18$311 million) in India. Of the total cost of tobacco, 88% was attributed to men.ConclusionsThe cost of tobacco use was many times more than the expenditures on tobacco control by the government of India and about 16% more than the total tax revenue from tobacco. The tobacco-attributable cost of tuberculosis was three times higher than the expenditure on tuberculosis control in India. The economic costs estimated here do not include the costs of premature mortality from tobacco use, which is known to comprise roughly 50% to 80% of the total economic cost of tobacco in many countries

WhisperX: time-accurate speech transcription of long-form audio

Large-scale, weakly-supervised speech recognition models, such as Whisper, have demonstrated impressive results on speech recognition across domains and languages. However, their application to long audio transcription via buffered or sliding window approaches is prone to drifting, hallucination & repetition; and prohibits batched transcription due to their sequential nature. Further, timestamps corresponding each utterance are prone to inaccuracies and word-level timestamps are not available out-of-the-box. To overcome these challenges, we present WhisperX, a time-accurate speech recognition system with word-level timestamps utilising voice activity detection and forced phoneme alignment. In doing so, we demonstrate state-of-the-art performance on long-form transcription and word segmentation benchmarks. Additionally, we show that pre-segmenting audio with our proposed VAD Cut & Merge strategy improves transcription quality and enables a twelvefold transcription speedup via batched inference. The code is available open-source

Evaluation of the economic impact of California's Tobacco Control Program: a dynamic model approach

ObjectiveTo evaluate the long-term net economic impact of the California Tobacco Control Program.MethodsThis study developed a series of dynamic models of smoking-caused mortality, morbidity, health status and healthcare expenditures. The models were used to evaluate the impact of the tobacco control programme. Outcomes of interest in the evaluation include net healthcare expenditures saved, years of life saved, years of treating smoking-related diseases averted and the total economic value of net healthcare savings and life saved by the programme. These outcomes are evaluated to 2079. Due to data limitations, the evaluations are conducted only for men.ResultsThe California Tobacco Control Program resulted in over 700,000 person-years of life saved and over 150,000 person-years of treatment averted for the 14.7 million male California residents alive in 1990. The value of net healthcare savings and years of life saved resulting from the programme was $22 billion or$107 billion in 1990 dollars, depending on how a year of life is discounted. If women were included, the impact would likely be much greater.ConclusionsThe benefits of California's Tobacco Control Program are substantial and will continue to accrue for many years. Although the programme has resulted in increased longevity and additional healthcare resources for some, this impact is more than outweighed by the value of the additional years of life. Modelling the programme's impact in a dynamic framework makes it possible to evaluate the multiple impacts that the programme has on life, health and medical expenditures

CRISPRi-based radiation modifier screen identifies long non-coding RNA therapeutic targets in glioma.

BackgroundLong non-coding RNAs (lncRNAs) exhibit highly cell type-specific expression and function, making this class of transcript attractive for targeted cancer therapy. However, the vast majority of lncRNAs have not been tested as potential therapeutic targets, particularly in the context of currently used cancer treatments. Malignant glioma is rapidly fatal, and ionizing radiation is part of the current standard-of-care used to slow tumor growth in both adult and pediatric patients.ResultsWe use CRISPR interference (CRISPRi) to screen 5689 lncRNA loci in human glioblastoma (GBM) cells, identifying 467 hits that modify cell growth in the presence of clinically relevant doses of fractionated radiation. Thirty-three of these lncRNA hits sensitize cells to radiation, and based on their expression in adult and pediatric gliomas, nine of these hits are prioritized as lncRNA Glioma Radiation Sensitizers (lncGRS). Knockdown of lncGRS-1, a primate-conserved, nuclear-enriched lncRNA, inhibits the growth and proliferation of primary adult and pediatric glioma cells, but not the viability of normal brain cells. Using human brain organoids comprised of mature neural cell types as a three-dimensional tissue substrate to model the invasive growth of glioma, we find that antisense oligonucleotides targeting lncGRS-1 selectively decrease tumor growth and sensitize glioma cells to radiation therapy.ConclusionsThese studies identify lncGRS-1 as a glioma-specific therapeutic target and establish a generalizable approach to rapidly identify novel therapeutic targets in the vast non-coding genome to enhance radiation therapy

RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure

We present RecD (Recommendation Deduplication), a suite of end-to-end infrastructure optimizations across the Deep Learning Recommendation Model (DLRM) training pipeline. RecD addresses immense storage, preprocessing, and training overheads caused by feature duplication inherent in industry-scale DLRM training datasets. Feature duplication arises because DLRM datasets are generated from interactions. While each user session can generate multiple training samples, many features' values do not change across these samples. We demonstrate how RecD exploits this property, end-to-end, across a deployed training pipeline. RecD optimizes data generation pipelines to decrease dataset storage and preprocessing resource demands and to maximize duplication within a training batch. RecD introduces a new tensor format, InverseKeyedJaggedTensors (IKJTs), to deduplicate feature values in each batch. We show how DLRM model architectures can leverage IKJTs to drastically increase training throughput. RecD improves the training and preprocessing throughput and storage efficiency by up to 2.48x, 1.79x, and 3.71x, respectively, in an industry-scale DLRM training system.Comment: Published in the Proceedings of the Sixth Conference on Machine Learning and Systems (MLSys 2023

Urine protein:creatinine ratio vs 24-hour urine protein for proteinuria management: analysis from the phase 3 REFLECT study of lenvatinib vs sorafenib in hepatocellular carcinoma

Background: Proteinuria monitoring is required in patients receiving lenvatinib, however, current methodology involves burdensome overnight urine collection. Methods: To determine whether the simpler urine protein:creatinine ratio (UPCR) calculated from spot urine samples could be accurately used for proteinuria monitoring in patients receiving lenvatinib, we evaluated the correlation between UPCR and 24-hour urine protein results from the phase 3 REFLECT study. Paired data (323 tests, 154 patients) were analysed. Results: Regression analysis showed a statistically significant correlation between UPCR and 24-hour urine protein (R2: 0.75; P < 2 × 10−16). A UPCR cut-off value of 2.4 had 96.9% sensitivity, 82.5% specificity for delineating between grade 2 and 3 proteinuria. Using this UPCR cut-off value to determine the need for further testing could reduce the need for 24-hour urine collection in ~74% of patients. Conclusion: Incorporation of UPCR into the current algorithm for proteinuria management can enable optimisation of lenvatinib treatment, while minimising patient inconvenience

Who Is Exposed to Secondhand Smoke? Self-Reported and Serum Cotinine Measured Exposure in the U.S., 1999–2006

This study presents self-reported and serum cotinine measures of exposure to secondhand smoke (SHS) for nonsmoking children, adolescents, and adults. Estimates are disaggregated by time periods and sociodemographic characteristics based on analyses of the 1999–2006 National Health and Nutrition Examination Survey. Self-reported exposure rates are found to be highest for children, followed by adolescents and adults. Important differences in exposure are found by socioeconomic characteristics. Using serum cotinine to measure exposure yields much higher prevalence rates than self-reports. Rates of SHS exposure remain high, but cotinine levels are declining for most groups

Interaction of Morphine and Selective Serotonin Receptor Inhibitors in Rats Experiencing Inflammatory Pain

Citalopram and paroxetine are selective serotonin reuptake inhibitors and also have antinociceptive effects. We investigated the antiallodynic and antihyperalgesic effects of intrathecally administered morphine, citalopram, paroxetine, and combinations thereof, in a rat model in which peripheral inflammation was induced by complete Freund's adjuvant (CFA). Drugs were intrathecally administered via direct lumbar puncture. Mechanical allodynia was measured using a Dynamic Plantar Aesthesiometer. Thermal hyperalgesia and cold allodynia were determined by measuring latency of paw withdrawal in response to radiant heat and cold water. Behavioral tests were run before and 15, 30, 45, and 60 min after intrathecal injection. Intraplantar injection of CFA produced mechanical allodynia, thermal hyperalgesia, and cold allodynia. Intrathecally administered morphine (0.3 or 1 µg) had antiallodynic or antihyperalgesic effects (24.0%-71.9% elevation). The effects of morphine were significantly increased when a combination of citalopram (100 µg) and paroxetine (100 µg) was added (35.2%-95.1% elevation). This rise was reversed by naloxone and methysergide. The effects of citalopram and paroxetine were also reversed by naloxone and methysergide. We suggest that the mu opioid receptor and serotonin receptors play major roles in production of the antiallodynic and antihyperalgesic effects of morphine, citalopram, paroxetine, and combinations thereof, in animals experiencing inflammatory pain