Unlocking The Mysteries of DNA Adducts with Artificial Intelligence.

Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade-off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e. g., mutations) or non-genetic (e. g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)-based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI-based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts' identification, prediction of biochemical routes for adduct formation, adducts' half-life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI-based approaches in DNA adduct biology.

deepGraphh: AI-driven web service for graph-based quantitative structure-activity relationship analysis.

Artificial intelligence (AI)-based computational techniques allow rapid exploration of the chemical space. However, representation of the compounds into computational-compatible and detailed features is one of the crucial steps for quantitative structure-activity relationship (QSAR) analysis. Recently, graph-based methods are emerging as a powerful alternative to chemistry-restricted fingerprints or descriptors for modeling. Although graph-based modeling offers multiple advantages, its implementation demands in-depth domain knowledge and programming skills. Here we introduce deepGraphh, an end-to-end web service featuring a conglomerate of established graph-based methods for model generation for classification or regression tasks. The graphical user interface of deepGraphh supports highly configurable parameter support for model parameter tuning, model generation, cross-validation and testing of the user-supplied query molecules. deepGraphh supports four widely adopted methods for QSAR analysis, namely, graph convolution network, graph attention network, directed acyclic graph and Attentive FP. Comparative analysis revealed that deepGraphh supported methods are comparable to the descriptors-based machine learning techniques. Finally, we used deepGraphh models to predict the blood-brain barrier permeability of human and microbiome-generated metabolites. In summary, deepGraphh offers a one-stop web service for graph-based methods for chemoinformatics.

Artificial intelligence uncovers carcinogenic human metabolites.

The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats owing to its constant exposure to a myriad of heterogeneous compounds. Despite the availability of innate DNA damage responses, some genomic lesions trigger malignant transformation of cells. Accurate prediction of carcinogens is an ever-challenging task owing to the limited information about bona fide (non-)carcinogens. We developed Metabokiller, an ensemble classifier that accurately recognizes carcinogens by quantitatively assessing their electrophilicity, their potential to induce proliferation, oxidative stress, genomic instability, epigenome alterations, and anti-apoptotic response. Concomitant with the carcinogenicity prediction, Metabokiller is fully interpretable and outperforms existing best-practice methods for carcinogenicity prediction. Metabokiller unraveled potential carcinogenic human metabolites. To cross-validate Metabokiller predictions, we performed multiple functional assays using Saccharomyces cerevisiae and human cells with two Metabokiller-flagged human metabolites, namely 4-nitrocatechol and 3,4-dihydroxyphenylacetic acid, and observed high synergy between Metabokiller predictions and experimental validations.

A Mediation Analysis Examining High Risk, Anticholinergic Medication Use, Delirium, and Dementia After Major Surgery.

INTRODUCTION: Anticholinergic medications are known to cause adverse cognitive effects in community-dwelling older adults and medical inpatients, including dementia. The prevalence with which such medications are prescribed in older adults undergoing major surgery is not well described nor is their mediating relationship with delirium and dementia. We sought to determine the prevalence of high-risk medication use in major surgery patients and their relationship with the subsequent development of dementia. METHODS: This was a retrospective cohort study which used data between January 2013 and December 2019, in a large midwestern health system, including sixteen hospitals. All patients over age 50 undergoing surgery requiring an inpatient stay were included. The primary exposure was the number of doses of anticholinergic medications delivered during the hospital stay. The primary outcome was a new diagnosis of Alzheimer's disease and related dementias at 1-y postsurgery. Regression methods and a mediation analysis were used to explore relationships between anticholinergic medication usage, delirium, and dementia. RESULTS: There were 39,665 patients included, with a median age of 66. Most patients were exposed to anticholinergic medications (35,957/39,665; 91%), and 7588/39,665 (19.1%) patients received six or more doses during their hospital stay. Patients with at least six doses of these medications were more likely to be female, black, and with a lower American Society of Anesthesiologists class. Upon adjusted analysis, high doses of anticholinergic medications were associated with increased odds of dementia at 1 y relative to those with no exposure (odds ratio 2.7; 95% confidence interval 2.2-3.3). On mediation analysis, postoperative delirium mediated the effect of anticholinergic medications on dementia, explaining an estimated 57.6% of their association. CONCLUSIONS: High doses of anticholinergic medications are common in major surgery patients and, in part via a mediating relationship with postoperative delirium, are associated with the development of dementia 1 y following surgery. Strategies to decrease the use of these medications and encourage the use of alternatives may improve long-term cognitive recovery.

Accuracy and consistency of publicly available Large Language Models as clinical decision support tools for the management of colon cancer.

BACKGROUND: Large Language Models (LLM; e.g., ChatGPT) may be used to assist clinicians and form the basis of future clinical decision support (CDS) for colon cancer. The objectives of this study were to (1) evaluate the response accuracy of two LLM-powered interfaces in identifying guideline-based care in simulated clinical scenarios and (2) define response variation between and within LLMs. METHODS: Clinical scenarios with "next steps in management" queries were developed based on National Comprehensive Cancer Network guidelines. Prompts were entered into OpenAI ChatGPT and Microsoft Copilot in independent sessions, yielding four responses per scenario. Responses were compared to clinician-developed responses and assessed for accuracy, consistency, and verbosity. RESULTS: Across 108 responses to 27 prompts, both platforms yielded completely correct responses to 36% of scenarios (n = 39). For ChatGPT, 39% (n = 21) were missing information and 24% (n = 14) contained inaccurate/misleading information. Copilot performed similarly, with 37% (n = 20) having missing information and 28% (n = 15) containing inaccurate/misleading information (p = 0.96). Clinician responses were significantly shorter (34 ± 15.5 words) than both ChatGPT (251 ± 86 words) and Copilot (271 ± 67 words; both p < 0.01). CONCLUSIONS: Publicly available LLM applications often provide verbose responses with vague or inaccurate information regarding colon cancer management. Significant optimization is required before use in formal CDS.

Catastrophic health expenditure and distress financing of breast cancer treatment in India: evidence from a longitudinal cohort study.

OBJECTIVE: To estimate the catastrophic health expenditure and distress financing of breast cancer treatment in India. METHODS: The unit data from a longitudinal survey that followed 500 breast cancer patients treated at Tata Memorial Centre (TMC), Mumbai from June 2019 to March 2022 were used. The catastrophic health expenditure (CHE) was estimated using households' capacity to pay and distress financing as selling assets or borrowing loans to meet cost of treatment. Bivariate and logistic regression models were used for analysis. FINDINGS: The CHE of breast cancer was estimated at 84.2% (95% CI: 80.8,87.9%) and distress financing at 72.4% (95% CI: 67.8,76.6%). Higher prevalence of CHE and distress financing was found among rural, poor, agriculture dependent households and among patients from outside of Maharashtra. About 75% of breast cancer patients had some form of reimbursement but it reduced the incidence of catastrophic health expenditure by only 14%. Nearly 80% of the patients utilised multiple financing sources to meet the cost of treatment. The significant predictors of distress financing were catastrophic health expenditure, type of patient, educational attainment, main income source, health insurance, and state of residence. CONCLUSION: In India, the CHE and distress financing of breast cancer treatment is very high. Most of the patients who had CHE were more likely to incur distress financing. Inclusion of direct non-medical cost such as accommodation, food and travel of patients and accompanying person in the ambit of reimbursement of breast cancer treatment can reduce the CHE. We suggest that city specific cancer care centre need to be strengthened under the aegis of PM-JAY to cater quality cancer care in their own states of residence. TRIAL REGISTRATION: CTRI/2019/07/020142 on 10/07/2019.

Evaluation of historical data on persistent organic pollutants and heavy metals in Lake Baikal: Implications for accumulation in marine environments.

Lake Baikal, the largest freshwater lake by volume, provides drinking water and aquatic food supplies to over 2.5 million people. However, the lake has been contaminated with recalcitrant pollutants released from surrounding industrial complexes, agriculture, and natural lands, thereby increasing the risk of their bioaccumulation in fish and seals. Yet, a collective analysis of historical concentration data and their bioaccumulation potential as well as what factors drive their accumulation in fish or seals remains largely unknown. We analyzed concentration data from 42 studies collected between 1985 and 2019 in water, sediment, fish, and seals of Lake Baikal. Heavy metals had the highest concentrations in water and biota followed closely by polycyclic aromatic hydrocarbons (PAHs) and organochlorines. Among organochlorines, polychlorinated biphenyls (PCBs) showed the highest levels in water, surpassing hexachlorocyclohexane (HCH) concentrations, particularly after normalizing to solubility. While naphthalene and phenanthrene exhibited the highest average concentrations among polycyclic aromatic hydrocarbons (PAHs), their relative concentrations significantly decreased upon solubility normalization. The analysis confirmed that bioconcentration and biomagnification of organochlorine pesticides, PCBs, PAHs, and heavy metals depend primarily on source strength to drive their concentration in water and secondarily on their chemical characteristics as evidenced by the higher concentrations of low-solubility PCBs and high molecular weight PAHs in water and sediment. The differential biomagnification patterns of Cu, Hg, and Zn compared to Pb are attributed to their distinct sources and bioavailability, with Cu, Hg, and Zn showing more pronounced biomagnification due to prolonged industrial release, in contrast to the declining Pb levels. Dibenzo-p-dioxins were detected in sediment and seals, but not in water or fish compartments. These data highlight the importance of addressing even low concentrations of organic and inorganic pollutants and the need for more consistent and frequent monitoring to ensure the future usability of this and other similar essential natural resources.

Socioeconomic and geographic variations of disabilities in India: evidence from the National Family Health Survey, 2019-21.

BACKGROUND: Increasing disability is of global and national concern. Lack of evidence on disability across socioeconomic groups and geographic levels (especially small areas) impeded interventions for these disadvantaged subgroups. We aimed to examine the socioeconomic and geographic variations in disabilities, namely hearing, speech, visual, mental, and locomotor, in Indian participants using cross-sectional data from the National Family Health Survey 2019-2021. METHODS: Using data from 27,93,971 individuals, we estimated age-sex-adjusted disability rates at the national and sub-national levels. The extent of socioeconomic variations in disabilities was explored using the Erreygers Concentration Index and presented graphically through a concentration curve. We adopted a four-level random intercept logit model to compute the variance partitioning coefficient (VPC) to assess the significance of each geographical unit in total variability. We also calculated precision-weighted disability estimates of individuals across 707 districts and showed their correlation with within-district or between-cluster standard deviation. RESULTS: We estimated the prevalence of any disability of 10 per 1000 population. The locomotor disability was common, followed by mental, speech, hearing, and visual. The concentration index of each type of disability was highest in the poorest wealth quintile households and illiterate 18 + individuals, confirming higher socioeconomic variations in disability rates. Clusters share the largest source of geographic variation for any disability (6.5%), hearing (5.8%), visual (24.3%), and locomotor (17.4%). However, States/Union Territories (UTs) account for the highest variation in speech (3.7%) and mental (6.5%) disabilities, where the variation at the cluster level becomes negligible. Districts with the highest disability rates were clustered in Madhya Pradesh, Maharashtra, Karnataka, Tamil Nadu, Telangana, and Punjab. Further, we found positive correlations between the district rates and cluster standard deviations (SDs) for disabilities. CONCLUSIONS: Though the growing disability condition in India is itself a concerning issue, wide variations across socioeconomic groups and geographic locations indicate the implementation of several policy-relevant implications focusing on these vulnerable chunks of the population. Further, the critical importance of small-area variations within districts suggests the design of strategies targeting these high-burden areas of disabilities.

Socioeconomic variation in the prevalence of pain by anatomical sites among middle-aged and older adults in India: a cross-sectional study.

BACKGROUND: Pain is a significant global public health concern, particularly among individuals aged 45 and above. Its impact on the overall lifestyle of the individuals varies depending on the affected anatomical parts. Despite its widespread impact, there is limited awareness of the attributes of pain, making effective pain management challenging, particularly in India. This study aims to estimate the prevalence and variation in pain in different anatomical sites among middle-aged and older adults in India. METHODS: A cross-sectional design was employed, utilising data from the first wave of the Longitudinal Aging Study in India (LASI), 2017-2018. The age-sex adjusted prevalence of pain by anatomical sites (the back, joints, and ankles) was estimated using a multivariate logistic regression model. RESULTS: 47% of individuals aged 45 years and above reported joint pain, 31% reported back pain and 20% suffered from ankle or foot pain. The prevalence of pain at all the anatomical sites increased with age and was reported higher among females. Relative to respondents aged 45-59 years, those aged 75 years and older exhibited a 41% higher likelihood of experiencing back pain (AOR: 1.41, 95% CI: 1.19-1.67), a 67% higher likelihood of joint pain (AOR: 1.67, 95% CI: 1.49-1.89), and a 32% higher likelihood of ankle/foot pain (AOR: 1.32, 95% CI: 1.16-1.50). In comparison to males, females had a 56% higher likelihood of encountering back pain (AOR: 1.56, 95% CI: 1.40-1.74), a 38% higher likelihood of joint pain (AOR: 1.38, 95% CI: 1.27-1.50), and a 35% higher likelihood of ankle/foot pain (AOR: 1.35, 95% CI: 1.17-1.57). We also found significant regional variations in pain prevalence, with higher rates in the mountainous regions of India. CONCLUSION: This research highlights the high burden of pain in major anatomical sites among middle-aged and older adults in India and emphasises the need for increased awareness and effective pain management strategies.

Quality of Life Outcomes in Stage IV Colorectal Cancer.

With improvements across the colorectal cancer care continuum, from screening and earlier detection to better systemic options, patients are living longer with the disease. Given these improvements over the last several decades, quality of life outcomes have become important components when evaluating treatment efficacy and adverse effects. This article reviews quality of life measurement generally, discusses tools currently being used in colorectal cancer patients, and reviews outcomes following both surgical and nonsurgical management from clinical trials, observational studies, and meta-analyses.

The Cellular basis of loss of smell in 2019-nCoV-infected individuals.

A prominent clinical symptom of 2019-novel coronavirus (nCoV) infection is hyposmia/anosmia (decrease or loss of sense of smell), along with general symptoms such as fatigue, shortness of breath, fever and cough. The identity of the cell lineages that underpin the infection-associated loss of olfaction could be critical for the clinical management of 2019-nCoV-infected individuals. Recent research has confirmed the role of angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2) as key host-specific cellular moieties responsible for the cellular entry of the virus. Accordingly, the ongoing medical examinations and the autopsy reports of the deceased individuals indicate that organs/tissues with high expression levels of ACE2, TMPRSS2 and other putative viral entry-associated genes are most vulnerable to the infection. We studied if anosmia in 2019-nCoV-infected individuals can be explained by the expression patterns associated with these host-specific moieties across the known olfactory epithelial cell types, identified from a recently published single-cell expression study. Our findings underscore selective expression of these viral entry-associated genes in a subset of sustentacular cells (SUSs), Bowman's gland cells (BGCs) and stem cells of the olfactory epithelium. Co-expression analysis of ACE2 and TMPRSS2 and protein-protein interaction among the host and viral proteins elected regulatory cytoskeleton protein-enriched SUSs as the most vulnerable cell type of the olfactory epithelium. Furthermore, expression, structural and docking analyses of ACE2 revealed the potential risk of olfactory dysfunction in four additional mammalian species, revealing an evolutionarily conserved infection susceptibility. In summary, our findings provide a plausible cellular basis for the loss of smell in 2019-nCoV-infected patients.

EcTracker: Tracking and elucidating ectopic expression leveraging large-scale scRNA-seq studies.

Dramatic genomic alterations, either inducible or in a pathological state, dismantle the core regulatory networks, leading to the activation of normally silent genes. Despite possessing immense therapeutic potential, accurate detection of these transcripts is an ever-challenging task, as it requires prior knowledge of the physiological gene expression levels. Here, we introduce EcTracker, an R-/Shiny-based single-cell data analysis web server that bestows a plethora of functionalities that collectively enable the quantitative and qualitative assessments of bona fide cell types or tissue-specific transcripts and, conversely, the ectopically expressed genes in the single-cell ribonucleic acid sequencing datasets. Moreover, it also allows regulon analysis to identify the key transcriptional factors regulating the user-selected gene signatures. To demonstrate the EcTracker functionality, we reanalyzed the CRISPR interference (CRISPRi) dataset of the human embryonic stem cells differentiated into endoderm lineage and identified the prominent enrichment of a specific gene signature in the SMAD2 knockout cells whose identity was ambiguous in the original study. The key distinguishing features of EcTracker lie within its processing speed, availability of multiple add-on modules, interactive graphical user interface and comprehensiveness. In summary, EcTracker provides an easy-to-perform, integrative and end-to-end single-cell data analysis platform that allows decoding of cellular identities, identification of ectopically expressed genes and their regulatory networks, and therefore, collectively imparts a novel dimension for analyzing single-cell datasets.

An Evaluation of Sex-Based Differences in Surrogate Consent for Older Adults Undergoing Surgical Intervention.

INTRODUCTION: Differences between female and male patients have been identified in many facets of medicine. We sought to understand whether differences in frequency of surrogate consent for operation exist between older female and male patients. MATERIALS AND METHODS: A descriptive study was designed using data from the hospitals participating in the American College of Surgeons National Surgical Quality Improvement Program. Patients age 65 y and older who underwent operation between 2014 and 2018 were included. RESULTS: Of 51,618 patients identified, 3405 (6.6%) had surrogate consent for surgery. Overall, 7.7% of females had surrogate consent compared to 5.3% of males (P < 0.001). Stratified analysis based on age categories showed no difference in surrogate consent between female and male patients aged 65-74 yy (2.3% versus 2.6%, P = 0.16), but higher rates of surrogate consent in females than males among patients aged 75-84 y old (7.3% versus 5.6%, P < 0.001) and age ≥85 y (29.7% versus 20.8%, P < 0.001). A similar relationship was seen between sex and preoperative cognitive status. There was no difference in preoperative cognitive impairment in female and male patients age 65-74 y (4.4% versus 4.6%, P = 0.58), but higher rates of preoperative cognitive impairment were seen in females than males for those age 75-84 (9.5% versus 7.4%, P < 0.001) and aged ≥85 y (29.4% versus 21.3%, P < 0.001). Matching for age and cognitive impairment, there was no significant difference between rate of surrogate consent in males and females. CONCLUSIONS: Female patients are more likely than males to undergo surgery with surrogate consent. This difference is not based on patient sex alone - females undergoing operation are older than their male counterparts and more likely to be cognitively impaired.

Utility of the Healthy Aging Brain Care Monitor as a Patient-Reported Symptom Monitoring Tool in Older Injury Survivors.

INTRODUCTION: The objective of this study was to evaluate the performance of the Healthy Aging Brain Care Monitor (HABC-M) as a patient-reported outcome tool to measure cognitive, functional, and psychological symptoms among older adults who sustained non-neurologic injuries requiring hospital admission. METHODS: We used data from a multicenter randomized controlled trial to evaluate the utility of the HABC-M Self-Report version in older patients recovering from traumatic injuries. A total of 143 patients without cognitive impairment were included in the analysis. Cronbach's alpha was used to measure the internal consistency, and Spearman's rank correlation test was used to evaluate the relationship of the HABC-M with standard measures of cognitive, functional, and psychological outcomes. RESULTS: The HABC-M subscales and the total scale showed satisfactory internal consistency (Cronbach's alpha = 0.64 to 0.77). The HABC-M cognitive subscale did not correlate with the Mini-Mental State Examination. The HABC-M functional and psychological subscales correlated with corresponding standard reference measures (|rs| = 0.24-0.59). CONCLUSIONS: The HABC-M Self-Report version is a practical alternative to administering multiple surveys to monitor functional and psychological sequelae in older patients recovering from recent non-neurologic injuries. Its clinical application may facilitate personalized, multidisciplinary care coordination among older trauma survivors without cognitive impairment.

Socio-economic variations of breast cancer treatment and discontinuation: a study from a public tertiary cancer hospital in Mumbai, India.

BACKGROUND: The study examined the socio-economic variation of breast cancer treatment and treatment discontinuation due to deaths and financial crisis. METHODS: We used primary data of 500 patients with breast cancer sought treatment at India's one of the largest cancer hospital in Mumbai, between June 2019 and March 2022. This study is registered on the Clinical Trial Registry of India (CTRI/2019/07/020142). Kaplan-Meier method and Cox-hazard regression model were used to calculate the probability of treatment discontinuation. RESULTS: Of the 500 patients, three-fifths were under 50 years, with the median age being 46 years. More than half of the patients were from outside of the state and had travelled an average distance of 1,044 kms to get treatment. The majority of the patients were poor with an average household income of INR15,551. A total of 71 (14%) patients out of 500 had discontinued their treatment. About 5.2% of the patients died and 4.8% of them discontinued treatment due to financial crisis. Over one-fourth of all deaths were reported among stage IV patients (25%). Patients who did not have any health insurance, never attended school, cancer stage IV had a higher percentage of treatment discontinuation due to financial crisis. Hazard of discontinuation was lower for patients with secondary (HR:0.48; 95% CI: 0.27-0.84) and higher secondary education (HR: 0.42; 95% CI: 0.19-0.92), patients from rural area (HR: 0.79; 95% CI: 0.42-1.50), treated under general or non-chargeable category (HR: 0.60; 95% CI:0.22-1.60) while it was higher for the stage IV patients (HR: 3.61; 95% CI: 1.58-8.29). CONCLUSION: Integrating breast cancer screening in maternal and child health programme can reduce delay in diagnosis and premature mortality. Provisioning of free treatment for poor patients may reduce discontinuation of treatment.

Out of pocket expenditure and distress financing on cesarean delivery in India: evidence from NFHS-5.

BACKGROUND: Though over three-fourths of all births receive medical attention in India, the rate of cesarean delivery (22%) is twice higher than the WHO recommended level. Cesarean deliveries entail high costs and may lead to financial catastrophe for households. This paper examines the out-of-pocket expenditure (OOPE) and distress financing of cesarean deliveries in India. METHODS: We used data from the latest round of the National Family Health Survey conducted during 2019-21. The survey covered 636,699 households, and 724,115 women in the age group 15-49 years. We have used 159,643 births those delivered three years preceding the survey for whom the question on cost was canvassed. Descriptive analysis, bivariate analysis, concentration index (CI), and concentration curve (CC) were used in the analysis. RESULT: Cesarean deliveries in India was estimated at 14.08%, in private health centres and 9.96%  in public health centres. The prevalence of cesarean delivery increases with age, educational attainment, wealth quintile, BMI and high for those who had pregnancy complications, and previous birth as cesarean. The OOPE on cesarean births was US$133. It was US$498 in private health centres and US$99 in public health centres. The extent of distress financing of any cesarean delivery was 15.37%; 27% for those who delivered in private health centres compared to 16.61% for those who delivered in public health centres. The odds of financial distress arising due to OOPE on cesarean delivery increased with the increase of OOPE [AOR:10.00, 95% CI, 9.35-10.70]. Distress financing increased with birth order and was higher among those with low education and those who belonged to lower socioeconomic strata. CONCLUSION: High OOPE on a cesarean delivery leads to distress financing in India. Timely monitoring of pregnancy and providing comprehensive pregnancy care, improving the quality of primary health centres to conduct cesarean deliveries, and regulating private health centres may reduce the high OOPE and financial distress due to cesarean deliveries in India.

Sociodemographic and geographic inequalities in diagnosis and treatment of older adults' chronic conditions in India: a nationally representative population-based study.

CONTEXT: Expeditious diagnosis and treatment of chronic conditions are critical to control the burden of non-communicable disease in low- and middle-income countries. We aimed to estimate sociodemographic and geographic inequalities in diagnosis and treatment of chronic conditions among adults aged 45 + in India. METHODS: We used 2017-18 nationally representative data to estimate prevalence of chronic conditions (hypertension, diabetes, lung disease, heart disease, stroke, arthritis, cholesterol, and neurological) reported as diagnosed and percentages of diagnosed conditions that were untreated by sociodemographic characteristics and state. We used concentration indices to measure socioeconomic inequalities in diagnosis and lack of treatment. Fully adjusted inequalities were estimated with multivariable probit and fractional regression models. FINDINGS: About 46.1% (95% CI: 44.9 to 47.3) of adults aged 45 + reported a diagnosis of at least one chronic condition and 27.5% (95% CI: 26.2 to 28.7) of the reported conditions were untreated. The percentage untreated was highest for neurological conditions (53.2%; 95% CI: 50.1 to 59.6) and lowest for diabetes (10.1%; 95% CI: 8.4 to 11.5). Age- and sex-adjusted prevalence of any diagnosed condition was highest in the richest quartile (55.3%; 95% CI: 53.3 to 57.3) and lowest in the poorest (37.7%: 95% CI: 36.1 to 39.3). Conditional on reported diagnosis, the percentage of conditions untreated was highest in the poorest quartile (34.4%: 95% CI: 32.3 to 36.5) and lowest in the richest (21.1%: 95% CI: 19.2 to 23.1). Concentration indices confirmed these patterns. Multivariable models showed that the percentage of untreated conditions was 6.0 points higher (95% CI: 3.3 to 8.6) in the poorest quartile than in the richest. Between state variations in the prevalence of diagnosed conditions and their treatment were large. CONCLUSIONS: Ensuring more equitable treatment of chronic conditions in India requires improved access for poorer, less educated, and rural older people who often remain untreated even once diagnosed.

OdoriFy: A conglomerate of artificial intelligence-driven prediction engines for olfactory decoding.

The molecular mechanisms of olfaction, or the sense of smell, are relatively underexplored compared with other sensory systems, primarily because of its underlying molecular complexity and the limited availability of dedicated predictive computational tools. Odorant receptors (ORs) allow the detection and discrimination of a myriad of odorant molecules and therefore mediate the first step of the olfactory signaling cascade. To date, odorant (or agonist) information for the majority of these receptors is still unknown, limiting our understanding of their functional relevance in odor-induced behavioral responses. In this study, we introduce OdoriFy, a Web server featuring powerful deep neural network-based prediction engines. OdoriFy enables (1) identification of odorant molecules for wildtype or mutant human ORs (Odor Finder); (2) classification of user-provided chemicals as odorants/nonodorants (Odorant Predictor); (3) identification of responsive ORs for a query odorant (OR Finder); and (4) interaction validation using Odorant-OR Pair Analysis. In addition, OdoriFy provides the rationale behind every prediction it makes by leveraging explainable artificial intelligence. This module highlights the basis of the prediction of odorants/nonodorants at atomic resolution and for the ORs at amino acid levels. A key distinguishing feature of OdoriFy is that it is built on a comprehensive repertoire of manually curated information of human ORs with their known agonists and nonagonists, making it a highly interactive and resource-enriched Web server. Moreover, comparative analysis of OdoriFy predictions with an alternative structure-based ligand interaction method revealed comparable results. OdoriFy is available freely as a web service at https://odorify.ahujalab.iiitd.edu.in/olfy/.

Machine-OlF-Action: a unified framework for developing and interpreting machine-learning models for chemosensory research.

SUMMARY: Machine Learning-based techniques are emerging as state-of-the-art methods in chemoinformatics to selectively, effectively and speedily identify biologically relevant molecules from large databases. So far, a multitude of such techniques have been proposed, but unfortunately due to their sparse availability, and the dependency on high-end computational literacy, their wider adaptation faces challenges, at least in the context of G-Protein Coupled Receptors (GPCRs)-associated chemosensory research. Here, we report Machine-OlF-Action (MOA), a user-friendly, open-source computational framework, that utilizes user-supplied SMILES (simplified molecular input line entry system) of the chemicals, along with their activation status, to synthesize classification models. MOA integrates a number of popular chemical databases collectively harboring approximately 103 million chemical moieties. MOA also facilitates customized screening of user-supplied chemical datasets. A key feature of MOA is its ability to embed molecules based on the similarity of their local neighborhood, by utilizing a state-of-the-art model interpretability framework LIME. We demonstrate the utility of MOA in identifying previously unreported agonists for human and mouse olfactory receptors OR1A1 and MOR174-9 by leveraging the chemical features of their known agonists and non-agonists. In summary, here we develop an ML-powered software playground for performing supervisory learning tasks involving chemical compounds. AVAILABILITY AND IMPLEMENTATION: MOA is available for Windows, Mac and Linux operating systems. It's accessible at (https://ahuja-lab.in/). Source code, user manual, step-by-step guide and support is available at GitHub (https://github.com/the-ahuja-lab/Machine-Olf-Action). For results, reproducibility and hyperparameters, refer to Supplementary Notes. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Missed opportunities for hypertension screening: a cross-sectional study, India.

OBJECTIVE: To assess missed opportunities for hypertension screening at health facilities in India and describe systematic differences in these missed opportunities across states and sociodemographic groups. METHODS: We used nationally representative survey data from the 2017-2018 Longitudinal Ageing Study in India to estimate the proportion of adults aged 45 years or older identified with hypertension and who had not been diagnosed with hypertension despite having visited a health facility during the previous 12 months. We estimated age-sex adjusted proportions of missed opportunities to diagnose hypertension, as well as actual and potential proportions of diagnosis, by sociodemographic characteristics and for each state. FINDINGS: Among those identified as having hypertension, 22.6% (95% confidence interval, CI: 21.3 to 23.8) had not been diagnosed despite having recently visited a health facility. If these opportunities had been realized, the prevalence of diagnosed hypertension would have increased from 54.8% (95% CI: 53.5 to 56.1) to 77.3% (95% CI: 76.2 to 78.5). Missed opportunities for diagnosis were more common among individuals who were poorer (P = 0.001), less educated (P < 0.001), male (P < 0.001), rural (P < 0.001), Hindu (P = 0.001), living alone (P = 0.028) and working (P < 0.001). Missed opportunities for diagnosis were more common at private than at public health facilities (P < 0.001) and varied widely across states (P < 0.001). CONCLUSION: Opportunistic screening for hypertension has the potential to significantly increase detection of the condition and reduce sociodemographic and geographic inequalities in its diagnosis. Such screening could be a first step towards more effective and equitable hypertension treatment and control.