Nevin Manimala

J Clin Oncol. 2023 Apr 19:JCO2201740. doi: 10.1200/JCO.22.01740. Online ahead of print.

ABSTRACT

PURPOSE: Although the incidence of young-onset digestive tract cancers is increasing worldwide, their risk factors remain largely unknown. We investigated the association between nonalcoholic fatty liver disease (NAFLD) and young-onset digestive tract cancers.

PATIENTS AND METHODS: This nationwide cohort study included 5,265,590 individuals age 20-39 years who underwent national health screening under the Korean National Health Insurance Service between 2009 and 2012. The fatty liver index was used as a diagnostic biomarker for NAFLD. The participants were followed up until December 2018 to determine the incidence of young-onset digestive tract cancers (ie, esophageal, stomach, colorectal, liver, pancreatic, biliary tract, and gallbladder). Multivariable Cox proportional hazards models were conducted to estimate the risk after adjusting for potential confounders.

RESULTS: During the 38.8 million person-years of follow-up, 14,565 patients were newly diagnosed with young-onset digestive tract cancers. The cumulative incidence probability of each cancer type was consistently higher in individuals with NAFLD than in those without NAFLD (all log-rank P < .05). NAFLD was associated with an increased risk of overall digestive tract (adjusted hazard ratio [aHR], 1.16; 95% CI, 1.10 to 1.22), stomach (aHR, 1.14; 95% CI, 1.06 to 1.24), colorectal (aHR, 1.14; 95% CI, 1.06 to 1.22), liver (aHR, 1.13; 95% CI, 1.12 to 1.52), pancreatic (aHR, 1.23; 95% CI, 1.09 to 1.40), biliary tract (aHR, 1.29; 95% CI, 1.00 to 1.66), and gallbladder (aHR, 1.53; 95% CI, 1.01 to 2.31) cancer. These associations remained significant regardless of age, sex, smoking status, alcohol consumption, and obesity status (all P < .05; P for interaction >.05). The aHR for esophageal cancer was 1.67 (95% CI, 0.92 to 3.03).

CONCLUSION: NAFLD may be an independent, modifiable risk factor for young-onset digestive tract cancers. Our findings suggest a crucial opportunity to reduce premature morbidity and mortality associated with young-onset digestive tract cancers in the next generation.

PMID:37075279 | DOI:10.1200/JCO.22.01740

J Chem Theory Comput. 2023 Apr 19. doi: 10.1021/acs.jctc.2c01018. Online ahead of print.

ABSTRACT

Interdependence across time and length scales is common in biology, where atomic interactions can impact larger-scale phenomenon. Such dependence is especially true for a well-known cancer signaling pathway, where the membrane-bound RAS protein binds an effector protein called RAF. To capture the driving forces that bring RAS and RAF (represented as two domains, RBD and CRD) together on the plasma membrane, simulations with the ability to calculate atomic detail while having long time and large length- scales are needed. The Multiscale Machine-Learned Modeling Infrastructure (MuMMI) is able to resolve RAS/RAF protein-membrane interactions that identify specific lipid-protein fingerprints that enhance protein orientations viable for effector binding. MuMMI is a fully automated, ensemble-based multiscale approach connecting three resolution scales: (1) the coarsest scale is a continuum model able to simulate milliseconds of time for a 1 μm² membrane, (2) the middle scale is a coarse-grained (CG) Martini bead model to explore protein-lipid interactions, and (3) the finest scale is an all-atom (AA) model capturing specific interactions between lipids and proteins. MuMMI dynamically couples adjacent scales in a pairwise manner using machine learning (ML). The dynamic coupling allows for better sampling of the refined scale from the adjacent coarse scale (forward) and on-the-fly feedback to improve the fidelity of the coarser scale from the adjacent refined scale (backward). MuMMI operates efficiently at any scale, from a few compute nodes to the largest supercomputers in the world, and is generalizable to simulate different systems. As computing resources continue to increase and multiscale methods continue to advance, fully automated multiscale simulations (like MuMMI) will be commonly used to address complex science questions.

PMID:37075065 | DOI:10.1021/acs.jctc.2c01018

PLoS One. 2023 Apr 19;18(4):e0284712. doi: 10.1371/journal.pone.0284712. eCollection 2023.

ABSTRACT

INTRODUCTION: Increasing use of CT examinations has led to concerns of possible negative cognitive effects for children. The objective of this study is to examine if the ionizing radiation dose from a CT head scan at the age of 6-16 years affects academic performance and high school eligibility at the end of compulsory school.

MATERIALS AND METHODS: A total of 832 children, 535 boys and 297 girls, from a previous trial where CT head scan was randomized on patients presenting with mild traumatic brain injury, were followed. Age at inclusion was 6-16 years (mean of 12.1), age at follow up 15-18 years (mean of 16.0), and time between injury and follow up one week up to 10 years (mean of 3.9). Participants’ radiation exposure status was linked with the total grade score, grades in mathematics and the Swedish language, eligibility for high school at the end of compulsory school, previously measured GOSE-score, and their mothers’ education level. The Chi-Square Test, Student’s t-Test and factorial logistics were used to analyze data.

RESULTS: Although estimates of school grades and high school eligibility were generally higher for the unexposed, the results showed no statistically significant differences between the exposed and unexposed participants in any of the aforementioned variables.

CONCLUSIONS: Any effect on high school eligibility and school grades from a CT head scan at the age of 6-16 years is too small to be detected in a study of more than 800 patients, half of whom were randomly assigned to CT head scan exposure.

PMID:37075051 | DOI:10.1371/journal.pone.0284712

PLoS One. 2023 Apr 19;18(4):e0283851. doi: 10.1371/journal.pone.0283851. eCollection 2023.

ABSTRACT

The Boston Marathon is one of the most prestigious running races in the world. From its inception in 1897, popularity grew to a point in 1970 where qualifying times were implemented to cap the number of participants. Currently, women’s qualifying times in each age group are thirty minutes slower than the men’s qualifying times equating to a 16.7% adjustment for the 18-34 age group, decreasing with age to a 10.4% adjustment for the 80+ age group. This setup somewhat counter-intuitively implies that women become faster with age relative to men. We present a data-driven approach to determine qualifying standards that lead to an equal proportion of qualifiers in each age category and gender. We had to exclude the 75-79 and 80+ age groups from analysis due to limited data. When minimizing the difference in proportion of men and women qualifying, the women’s times for the 65-69 and 70-74 age groups are 4-5 minutes slower than the current qualifying standard, while they are 0 to 3 minutes faster for all other age groups.

PMID:37075050 | DOI:10.1371/journal.pone.0283851

PLoS One. 2023 Apr 19;18(4):e0284382. doi: 10.1371/journal.pone.0284382. eCollection 2023.

ABSTRACT

BACKGROUND: Childhood stunting is still a global public health challenge, including in Ethiopia. Over the past decade, in developing countries, stunting has been characterized by large rural and urban disparities. To design an effective intervention, it is necessary to understand the urban and rural disparities in stunting.

OBJECTIVE: To assess the urban-rural disparities in stunting among Ethiopian children aged 6-59 months.

METHODS: This study was done based on the data obtained from the 2019 mini-Ethiopian Demographic and Health Survey, conducted by the Central Statistical Agency of Ethiopia and ICF international. The result of descriptive statistics was reported using the mean with standard deviation, frequency, percentages, graphs, and tables. A multivariate decomposition analysis was used to decompose the urban-rural disparity in stunting into two components: one that is explained by residence differences in the level of the determinants (covariate effects), and the other component is explained by differences in the effect of the covariates on the outcome (coefficient effects). The results were robust to the different decomposition weighting schemes.

RESULT: The prevalence of stunting among Ethiopian children aged 6-59 months was 37.8% (95% CI: 36.8%, 39.6%). The difference in stunting prevalence between urban and rural residences was high (rural prevalence was 41.5%, while in urban areas it was 25.5%). Endowment and coefficient factors explained the urban-rural disparity in stunting with magnitudes of 35.26% and 64.74%, respectively. Maternal educational status, sex, and age of children were the determinants of the urban-rural disparity in stunting.

CONCLUSION AND RECOMMENDATION: There is a significant stunting disparity among urban and rural children in Ethiopia. A larger portion of the urban-rural stunting disparity was explained by coefficient effects (differences in behaviour). Maternal educational status, sex, and age of children were the determinants of the disparity. So, to narrow this disparity, emphasis should be given to both resource distribution and the appropriate utilization of available interventions, including improvement of maternal education and consideration of sex and age differences during child feeding practices.

PMID:37075042 | DOI:10.1371/journal.pone.0284382

PLoS One. 2023 Apr 19;18(4):e0283251. doi: 10.1371/journal.pone.0283251. eCollection 2023.

ABSTRACT

The utilization or secondary use of technogenic waste is a relevant problem for the current economy. To assess the environmental influence and economic potential, it is necessary to study the elemental content of technogenic objects and to reveal the tendencies of the spatial distribution of elements, components, and indices such as the pollution coefficient. In this study, we performed elemental analysis, and calculation of indicators: average gross content, hazard quotients, concentration coefficients of metals, and total pollution coefficients of ground samples taken from the ash-slag storage of the Aksu ferroalloy plant [Aksu, Pavlodar region, Kazakhstan]. Maps of the spatial distribution of concentrations of elements and total pollution coefficients were created. The territory of the studied ash-slag storage by the level of soil contamination should be considered as an environmental disaster zone. The given statistical data on the number of oncological and respiratory diseases indirectly indicated the negative influence of open storage of ash-slag waste. The studied ground was of chromium-manganese geochemical specialization. The calculated volume of the accumulated waste mass by the approximating method was 1 054 638.0 m3. The calculated approximate weight of the accumulated waste was 23 679 576.0864 tons, including 1 822 972.2 tons of chromium, 1 727 354.0 tons of manganese, and 953 813.3 tons of iron. The large amounts of valuable components retained in the waste mass led us to conclude that the studied technogenic object can be considered as a secondary field to produce various technological products. Moreover, valuable metals can be extracted as metal concentrates.

PMID:37074997 | DOI:10.1371/journal.pone.0283251

PLoS One. 2023 Apr 19;18(4):e0283848. doi: 10.1371/journal.pone.0283848. eCollection 2023.

ABSTRACT

Soundscape ecology provides a long-term, noninvasive approach to track animal behavior, habitat quality, and community structure over temporal and spatial scales. Using soniferous species as an indicator, biological soundscapes provide information about species and ecosystem health as well as their response and resiliency to potential stressors such as noise pollution. Charleston Harbor, South Carolina, USA provides important estuarine habitat for an abundance of marine life and is one of the busiest and fastest growing container ports in the southeast USA. Six passive acoustic recorders were deployed in the Charleston Harbor from December 2017 to June 2019 to determine biological patterns and human-associated influences on the soundscape. Anthropogenic noise was detected frequently across the estuary, especially along the shipping channel. Despite this anthropogenic noise, biological sound patterns were identified including snapping shrimp snaps (Alpheus spp. and Synalpheus spp.), fish calling and chorusing (Sciaenidae and Batrachoididae families), and bottlenose dolphin vocalizations. Biological response to anthropogenic activity varied among trophic levels, with decreased detection of fish calling when anthropogenic noise occurred and increased dolphin vocalizations in the presence of anthropogenic noise. Statistically, fine-scale, temporal patterns in biological sound were not clearly identified by sound pressure levels (SPLs), until files with anthropogenic noise presence were removed. These findings indicate that SPL patterns may be limited in their interpretation of biological activity for noisy regions and that the overall acoustic signature that we find in more pristine estuaries is lost in Charleston Harbor.

PMID:37074986 | DOI:10.1371/journal.pone.0283848

J Natl Cancer Inst. 2023 Apr 19:djad069. doi: 10.1093/jnci/djad069. Online ahead of print.

ABSTRACT

BACKGROUND: Starting in 2018, national death certificates included a new racial classification system that accounts for multiple-race decedents and separates Native Hawaiian and Pacific Islander (NHPI) individuals from Asian individuals. We estimated cancer death rates across updated racial/ethnic categories, sex, and age.

METHODS: Age-standardized U.S. cancer mortality rates and rate ratios from 2018-2020 among ≥20-year-olds were estimated with national death certificate data by race/ethnicity, sex, age, and cancer site.

RESULTS: In 2018, there were approximately 597,000 cancer deaths, 598,000 in 2019, and 601,000 in 2020. Among men, cancer death rates were highest in Black men (298.2/100,000; n = 105,632), followed by White (250.8; n = 736,319), American Indian/Alaska Native (AI/AN) (249.2; n = 3,376), NHPI (205.6; n = 1,080), Latino (177.2; n = 66,167), and Asian (147.9; n = 26,591) men. Among women, Black women had the highest cancer death rates (206.5/100,000; n = 104,437), followed by NHPI (192.1; n = 1,141), AI/AN (189.9; n = 3,239), White (183.0; n = 646,865), Latina (128.4; n = 61,579), and Asian women (111.4; n = 26,396). The highest death rates by age group occurred among NHPI individuals aged 20-49 years, and Black individuals aged 50-69 and ≥70 years. Asian individuals had the lowest cancer death rates across age groups. Compared to Asian individuals, total cancer death rates were 39% higher in NHPI men and 73% higher in NHPI women.

CONCLUSIONS: There were striking racial/ethnic disparities in cancer death rates during 2018-2020. Separating NHPI and Asian individuals revealed large differences in cancer mortality between two groups that were previously combined in vital statistics data.

PMID:37074947 | DOI:10.1093/jnci/djad069

Bioinformatics. 2023 Apr 19:btad214. doi: 10.1093/bioinformatics/btad214. Online ahead of print.

ABSTRACT

SUMMARY: PyHMMER provides Python integration of the popular profile Hidden Markov Model software HMMER via Cython bindings. This allows annotation of protein sequences with profile HMMs and building new ones directly with Python. PyHMMER increases flexibility of use, allowing creating queries directly from Python code, launching searches and obtaining results without I/O, or accessing previously unavailable statistics like uncorrected p-values. A new parallelization model greatly improves performance when running multithreaded searches, while producing the exact same results as HMMER.

AVAILABILITY AND IMPLEMENTATION: PyHMMER supports all modern Python versions (Python 3.6+) and similar platforms as HMMER (x86 or PowerPC UNIX systems). Pre-compiled packages are released via PyPI (https://pypi.org/project/pyhmmer/) and Bioconda (https://anaconda.org/bioconda/pyhmmer). The PyHMMER source code is available under the terms of the open-source MIT licence and hosted on GitHub (https://github.com/althonos/pyhmmer); its documentation is available on ReadTheDocs (https://pyhmmer.readthedocs.io).

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:37074928 | DOI:10.1093/bioinformatics/btad214