The Burden of Chronic Diseases with the Status of Family Medical History Among Older Adults in India.

This study aims to assess and compare the prevalence of chronic diseases by the first-degree Family Medical History (FMH) and also explores the relationship between FMH and selected Non-communicable diseases (NCDs) among older adults in India. The present study collated secondary data from the Longitudinal Ageing Study in India (LASI, 2017-18). The eligible respondents for the analysis of this study were aged 45 years and above, where the final study sample consisted of 65,562 older adults across all Indian states and union territories. The LASI dataset collected responses on self-reported diseases: Hypertension, Stroke, Heart disease, Cancer, and Diabetes. These diseases have a high prevalence among the population and are considered in the present study. Along with disease status, respondents' first-degree relatives FMH were used to fulfil the objective. Descriptive statistical analysis and multiple logistic regression techniques were used to accomplish the objectives analysis. This approach was chosen due to the binary nature of our primary dependent variables. The study found that the prevalence of selected NCDs was considerably higher among older adults with FMH than those without FMH. It revealed that NCDs and the status of FMH of parents and siblings were significantly associated. Based on the multivariate-adjusted model, we found significantly higher odds for developing the NCDs when the respondents have FMH among at least one of the first-degree relative. The likelihood among those with FMH of having hypertension (AOR: 2.058), diabetes (AOR: 2.94), heart diseases (AOR: 2.39), stroke (AOR: 1.62) and cancer (AOR: 2.32) was higher compared to no FMH of respective diseases. Similarly, significant associations were observed according to the different stratification of the number of first-degree relatives FMH. The present study demonstrated that first-degree relatives FMH is indeed a dominant associated risk factor for chronic disease among the older adults of India. This study supports the promotion of a disease history tool for chronic disease prevention and early detection approaches as a valuable measure of NCD risk. Public health practitioners can take several steps to access FMH and incorporate FMH into public health programs for the screening of the risk population.

PROTACable Is an Integrative Computational Pipeline of 3-D Modeling and Deep Learning To Automate the De Novo Design of PROTACs.

Proteolysis-targeting chimeras (PROTACs) that engage two biological targets at once are a promising technology in degrading clinically relevant protein targets. Since factors that influence the biological activities of PROTACs are more complex than those of a small molecule drug, we explored a combination of computational chemistry and deep learning strategies to forecast PROTAC activity and enable automated design. A new method named PROTACable was developed for the de novo design of PROTACs, which includes a robust 3-D modeling workflow to model PROTAC ternary complexes using a library of E3 ligase and linker and an SE(3)-equivariant graph transformer network to predict the activity of newly designed PROTACs. PROTACable is available at https://github.com/giaguaro/PROTACable/.

Exploring the financial burden due to additional mobility among cancer patients: A cross-sectional study based on National Sample Survey.

BACKGROUND: Cancer imposes a substantial economic burden due to treatment costs, supportive care, and loss of productivity. Besides all the affecting factors, major concerns lead to significant financial burdens of cancer treatment, bringing unwanted huge unbearable direct and indirect treatment costs. The aim was to explore the nature of additional mobility/travel required for accessing health care for cancer patients and also to assess financial burden due to additional mobility/travel costs for cancer treatment. METHODS: This study employed unit-level cross-sectional data from the 75th round (2017-18) of India's National Sample Survey (NSS). The primary analysis commenced with descriptive and bivariate analyses to explore mean health spending and out-of-pocket expenses. Subsequently, multivariable logistic regression models were utilized to estimate the associations between catastrophic health expenditure, distress financing, and the treatment location. RESULTS: The findings highlight distinct healthcare utilization patterns: inpatient treatments predominantly occur within the same district (50.4 %), followed by a different district (38.8 %), and a smaller share in other states (10.8 %). Outpatients largely receive treatment in the same district (65.5 %), followed by a different district (26.8 %), and around 8 % percent in other states. Urban areas show higher inpatient visits within the same district (41.8 %) and different districts (33.5 %). Outpatients, particularly those seeking treatment in other states, experience higher total expenditures, notably with higher out-of-pocket expenses. Distress financing is more common among inpatients (20.6 %) and combined inpatient/outpatient cases (23.9 %), while outpatients exhibit a lower rate (6.8 %). CONCLUSION: The findings collectively suggest the importance of developing local healthcare infrastructures to reduce the additional mobility of cancer patients. The policy should focus to train and deploy oncologists in non-urban areas can help bridge the gap in cancer care proficiency and reduce the need for patients to travel long distances for treatment.

Health and disability status among middle-aged and older adult cancer survivors: a case-control study.

PURPOSE: The objective of this study is to evaluate whether the presence of a cancer history constitutes a risk for encountering unfavourable health outcomes and functional limitations. Moreover, the study also aims to identify specific attributes of cancer survivors that are associated with an increased risk of experiencing poor health and disability. METHODS: This study has utilized data from Longitudinal Ageing Study in India (LASI) conducted in 2017-18. The analytical sample size for this study was 65,562 older individuals of age 45 years and above. We have focused on individuals diagnosed with cancer, i.e., cancer survivors, and compared their health outcomes to those of a similar group (without a cancer history) with similar socioeconomic and demographic features. Descriptive statistics and logistic regression models were used to assess the adjusted effect of explanatory variables on cancer survivors. RESULTS: The result shows that the overall number of cancer survivors is 673 per 100.000 older adults and is higher in Urban areas (874 per 100.000) than in rural areas (535 per 100.000). 43.7% of the survivors reported poor self-rated health, and around 34.0% of cancer survivors reported depression, while this prevalence was much lower among older adults without a cancer history. Individuals who were diagnosed with cancer a long time ago have a significantly lower likelihood of experiencing poor SRH, depression, and diminished life satisfaction in comparison to those diagnosed more recently. CONCLUSION: The study highlights the importance of factors such as time since diagnosis and the number of cancer sites in influencing health outcomes among survivors. Additionally, socioeconomic factors, such as wealth and access to health insurance, appear to play a role in the health status of cancer survivors. IMPLICATIONS FOR CANCER SURVIVORS: Healthcare policies should recognize the long-term impact of cancer and prioritize the provision of long-term survivorship care. This may involve establishing survivorship clinics or dedicated healthcare centres that provide specialized care for cancer survivors, addressing their unique needs throughout the survivorship continuum.

Two Decadal Experiences in Managing Combined Obstetric Vesicovaginal and Rectovaginal Fistulas: A Study From Northern Indian Tertiary Hospital.

INTRODUCTION: A retrospective study of 28 patients with obstetric combined vesicovaginal fistula (VVF) and rectovaginal fistula (RVF) treated at our centre throughout the last two decades (2002 to 2022) has been conducted. MATERIAL AND METHOD: In 12 patients, a preoperative diverting colostomy was performed. Six patients had single-stage surgery (both VVF and RVF repair in the same operation) of which two cases required transabdominal repair and four required transvaginal repair. RESULT: All single-stage repairs (n=6) were successful in curing urine and faecal incontinence. In 22 patients, VVF was corrected initially via the transvaginal method with Martius flap interposition, followed by RVF repair three months later. In 2/22 patients, there was a leak after RVF repair; therefore, proximal diverting colostomy was performed, and RVF repair was repeated after six months. CONCLUSION: All cases had effective VVF and RVF repairs, and both urine and faecal incontinence were completely cured. This study suggests the collaborative engagement of a urologist and a surgical gastroenterologist results in an advantageous outcome for the surgical treatment of these intricate obstetric fistulas.

An assessment of self-rated life satisfaction and its correlates with physical, mental and social health status among older adults in India.

Life satisfaction refers to the assessment of one's own life in terms of self-perceived favourable qualities. It is an integral part of healthy and successful course of ageing. It is widely associated with the health status and social well-being. The present study attempted to determine the constructing factors of self-rated life satisfaction, such as socio-demographic, physical, social, and mental well-being of older adults. We analysed information from the Longitudinal Ageing Study in India (LASI-1), the initial phase conducted during 2017-18 for the population of older adults in India. We applied descriptive statistics for prevalence assessment and association was checked using chi-square test. Further, to determine the adjusted outcome of predictor covariates on the likelihood of an individual being satisfied from life estimated by applying hierarchical multiple logistic regression models. Several noteworthy affirmations on the relationship between the socio-demographic variables and health risk behaviours with life satisfaction were observed. The results were consistent with studies showing change in life satisfaction subject to the state of physical and mental health, presence of chronic diseases, friends and family relations, dependency, and events of trauma or abuse. While comparing respondents, we found varying degrees of life satisfaction by gender, education, marital status, expenditure and other socio-economic features. We also found that besides physical and mental health, social support and well-being play a pivotal role in achieving higher life satisfaction among older adults. Overall, this work contributes to the study of the subjective well-being of older adults in India based on self-reported levels of life satisfaction and further narrows the gap in knowledge about associated behaviour. Hence, with on-going ageing scenario, there is need for multi-sectorial policy-oriented approaches at individual, family, and community level, which helps to take care of older-adults' physical, social, and mental health for the betterment of healthy ageing.

Novel Inhibitors of androgen receptor's DNA binding domain identified using an ultra-large virtual screening.

Androgen receptor (AR) inhibition remains the primary strategy to combat the progression of prostate cancer (PC). However, all clinically used AR inhibitors target the ligand-binding domain (LBD), which is highly susceptible to truncations through splicing or mutations that confer drug resistance. Thus, there exists an urgent need for AR inhibitors with novel modes of action. We thus launched a virtual screening of an ultra-large chemical library to find novel inhibitors of the AR DNA-binding domain (DBD) at two sites: protein-DNA interface (P-box) and dimerization site (D-box). The compounds selected through vigorous computational filtering were then experimentally validated. We identified several novel chemotypes that effectively suppress transcriptional activity of AR and its splice variant V7. The identified compounds represent previously unexplored chemical scaffolds with a mechanism of action that evades the conventional drug resistance manifested through LBD mutations. Additionally, we describe the binding features required to inhibit AR DBD at both P-box and D-box target sites.

PurificationDB: database of purification conditions for proteins.

The isolation of proteins of interest from cell lysates is an integral step to study protein structure and function. Liquid chromatography is a technique commonly used for protein purification, where the separation is performed by exploiting the differences in physical and chemical characteristics of proteins. The complex nature of proteins requires researchers to carefully choose buffers that maintain stability and activity of the protein while also allowing for appropriate interaction with chromatography columns. To choose the proper buffer, biochemists often search for reports of successful purification in the literature; however, they often encounter roadblocks such as lack of accessibility to journals, non-exhaustive specification of components and unfamiliar naming conventions. To overcome such issues, we present PurificationDB (https://purificationdatabase.herokuapp.com/), an open-access and user-friendly knowledge base that contains 4732 curated and standardized entries of protein purification conditions. Buffer specifications were derived from the literature using named-entity recognition techniques developed using common nomenclature provided by protein biochemists. PurificationDB also incorporates information associated with well-known protein databases: Protein Data Bank and UniProt. PurificationDB facilitates easy access to data on protein purification techniques and contributes to the growing effort of creating open resources that organize experimental conditions and data for improved access and analysis. Database URL https://purificationdatabase.herokuapp.com/.

Large-Scale Virtual Screening for the Discovery of SARS-CoV-2 Papain-like Protease (PLpro) Non-covalent Inhibitors.

The rapid global spread of the SARS-CoV-2 virus facilitated the development of novel direct-acting antiviral agents (DAAs). The papain-like protease (PLpro) has been proposed as one of the major SARS-CoV-2 targets for DAAs due to its dual role in processing viral proteins and facilitating the host's immune suppression. This dual role makes identifying small molecules that can effectively neutralize SARS-CoV-2 PLpro activity a high-priority task. However, PLpro drug discovery faces a significant challenge due to the high mobility and induced-fit effects in the protease's active site. Herein, we virtually screened the ZINC20 database with Deep Docking (DD) to identify prospective noncovalent PLpro binders and combined ultra-large consensus docking with two pharmacophore (ph4)-filtering strategies. The analysis of active compounds revealed their somewhat-limited diversity, likely attributed to the induced-fit nature of PLpro's active site in the crystal structures, and therefore, the use of rigid docking protocols poses inherited limitations. The top hits were assessed against recombinant viral proteins and live viruses, demonstrating desirable inhibitory activities. The best compound VPC-300195 (IC50: 15 µM) ranks among the top noncovalent PLpro inhibitors discovered through in silico methodologies. In the search for novel SARS-CoV-2 PLpro-specific chemotypes, the identified inhibitors could serve as diverse templates for the development of effective noncovalent PLpro inhibitors.

Tumorous kidney segmentation in abdominal CT images using active contour and 3D-UNet.

BACKGROUND AND PURPOSE: The precise segmentation of the kidneys in computed tomography (CT) images is vital in urology for diagnosis, treatment, and surgical planning. Medical experts can get assistance through segmentation, as it provides information about kidney malformations in terms of shape and size. Manual segmentation is slow, tedious, and not reproducible. An automatic computer-aided system is a solution to this problem. This paper presents an automated kidney segmentation technique based on active contour and deep learning. MATERIALS AND METHODS: In this work, 210 CTs from the KiTS 19 repository were used. The used dataset was divided into a train set (168 CTs), test set (21 CTs), and validation set (21 CTs). The suggested technique has broadly four phases: (1) extraction of kidney regions using active contours, (2) preprocessing, (3) kidney segmentation using 3D U-Net, and (4) reconstruction of the segmented CT images. RESULTS: The proposed segmentation method has received the Dice score of 97.62%, Jaccard index of 95.74%, average sensitivity of 98.28%, specificity of 99.95%, and accuracy of 99.93% over the validation dataset. CONCLUSION: The proposed method can efficiently solve the problem of tumorous kidney segmentation in CT images by using active contour and deep learning. The active contour was used to select kidney regions and 3D-UNet was used for precisely segmenting the tumorous kidney.

Targeting SARS-CoV-2 papain-like protease in the postvaccine era.

While vaccines remain at the forefront of global healthcare responses, pioneering therapeutics against SARS-CoV-2 are expected to fill the gaps for waning immunity. Rapid development and approval of orally available direct-acting antivirals targeting crucial SARS-CoV-2 proteins marked the beginning of the era of small-molecule drugs for COVID-19. In that regard, the papain-like protease (PLpro) can be considered a major SARS-CoV-2 therapeutic target due to its dual biological role in suppressing host innate immune responses and in ensuring viral replication. Here, we summarize the challenges of targeting PLpro and innovative early-stage PLpro-specific small molecules. We propose that state-of-the-art computer-aided drug design (CADD) methodologies will play a critical role in the discovery of PLpro compounds as a novel class of COVID-19 drugs.

Association of multi-morbidity, social participation, functional and mental health with the self-rated health of middle-aged and older adults in India: a study based on LASI wave-1.

Self-rated health (SRH) is a well-established measure in public health to administer the general health of an individual. It can also be used to assess overall health status' relationship with the social, physical, and mental health of a person. In this study, we examine the association of SRH and various socio-economic & health-related factors such as multi-morbidity status, mental health, functional health, and social participation. Data used in this paper is collated from the first wave of Longitudinal Ageing Study in India (LASI) 2017-18. A total of 65,562 older adults aged 45 or above are considered in our study. Various indices (multimorbidity, social participation, functional and mental health) have been created to measure factors influencing the SRH of an individual. Overall, in the study population, around 18.4% of people reported poor SRH. Dominance Analysis results show that the contribution of multimorbidity in predicting poor SRH is highest, followed by functional health, mental health, and social participation. In a developing country like India, there is a dire need for policies having a holistic approach regarding the health and well-being of the older population.

Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks.

Computational prediction of ligand-target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph-Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions that are most important for ligand binding. Results: The developed approach demonstrates the state-of-the-art performance on several binding affinity benchmarking datasets. The attention-based pooling of protein graphs enables identification of surface residues as critical residues for protein-ligand binding. Finally, we validate our model predictions against an experimental assay on a viral main protease (Mpro)-the hallmark target of SARS-CoV-2 coronavirus.

Differential progression of coronary atherosclerosis according to plaque composition: a cluster analysis of PARADIGM registry data.

Patient-specific phenotyping of coronary atherosclerosis would facilitate personalized risk assessment and preventive treatment. We explored whether unsupervised cluster analysis can categorize patients with coronary atherosclerosis according to their plaque composition, and determined how these differing plaque composition profiles impact plaque progression. Patients with coronary atherosclerotic plaque (n = 947; median age, 62 years; 59% male) were enrolled from a prospective multi-national registry of consecutive patients who underwent serial coronary computed tomography angiography (median inter-scan duration, 3.3 years). K-means clustering applied to the percent volume of each plaque component and identified 4 clusters of patients with distinct plaque composition. Cluster 1 (n = 52), which comprised mainly fibro-fatty plaque with a significant necrotic core (median, 55.7% and 16.0% of the total plaque volume, respectively), showed the least total plaque volume (PV) progression (+ 23.3 mm3), with necrotic core and fibro-fatty PV regression (- 5.7 mm3 and - 5.6 mm3, respectively). Cluster 2 (n = 219), which contained largely fibro-fatty (39.2%) and fibrous plaque (46.8%), showed fibro-fatty PV regression (- 2.4 mm3). Cluster 3 (n = 376), which comprised mostly fibrous (62.7%) and calcified plaque (23.6%), showed increasingly prominent calcified PV progression (+ 21.4 mm3). Cluster 4 (n = 300), which comprised mostly calcified plaque (58.7%), demonstrated the greatest total PV increase (+ 50.7mm3), predominantly increasing in calcified PV (+ 35.9 mm3). Multivariable analysis showed higher risk for plaque progression in Clusters 3 and 4, and higher risk for adverse cardiac events in Clusters 2, 3, and 4 compared to that in Cluster 1. Unsupervised clustering algorithms may uniquely characterize patient phenotypes with varied atherosclerotic plaque profiles, yielding distinct patterns of progressive disease and outcome.

An assessment of anthropometric indices and its association with NCDs among the older adults of India: evidence from LASI Wave-1.

BACKGROUND: The purpose of this study is to assess the status of physical body indices such as body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR) among the older adults aged 45 and above in India. Further, to explore the association of anthropometric indices with various non-communicable morbidities. METHODS: The study uses secondary data of the Longitudinal Ageing Survey's first wave in India (2017-18). The national representative sample for older adults 45 and above (65,662) considered for the analysis. The prevalence of the non-communicable diseases (NCDs) included in the study is based on the self-reporting of the participants. Diseases included are among the top ten causes of death, such as cancer, hypertension, stroke, chronic heart diseases, diabetes, chronic respiratory diseases, and multi-morbidity. Multi-morbidity is a case of having more than one of the morbidities mentioned above. BMI-obese indicates an individual having a BMI ≥30, and the critical threshold value for high-risk WC for men is ≥102 cm while for women is ≥88 cm. The critical limit for the high-risk WHR for men and women is ≥0.90 and ≥ 0.85, respectively. Descriptive statistics and multiple logistic regressions are used to assess the association BMI, WC, and WHR with non-communicable morbidities. RESULTS: Based on the multivariate-adjusted model, odds shows that an Indian older adult aged 45 and above is 2.3 times more likely (AOR: 2.33; 95% CI (2.2, 2.5)) by obesity, 61% more likely (AOR: 1.61; 95% CI (1.629, 1.631)) by high-risk WHR and 98% more likely (AOR: 1.98; 95% CI (1.9, 2.1)) by high-risk WC to develop CVDs than their normal counterparts. Similarly, significant positive associations of obesity, high-risk WC, and high-risk WHR were observed with other NCDs and multi-morbidity. CONCLUSION: Our study shows that obesity, high-risk WC, and high-risk WHR are significant risks for developing NCDs and multi-morbidity among the older adults in India. There is a need for a multi-sectoral approach to reduce the share of the elderly population in high-risk groups of BMIs, WHR, and WC.

Persistent dark states in anisotropic central spin models.

Long-lived dark states, in which an experimentally accessible qubit is not in thermal equilibrium with a surrounding spin bath, are pervasive in solid-state systems. We explain the ubiquity of dark states in a large class of inhomogeneous central spin models using the proximity to integrable lines with exact dark eigenstates. At numerically accessible sizes, dark states persist as eigenstates at large deviations from integrability, and the qubit retains memory of its initial polarization at long times. Although the eigenstates of the system are chaotic, exhibiting exponential sensitivity to small perturbations, they do not satisfy the eigenstate thermalization hypothesis. Rather, we predict long relaxation times that increase exponentially with system size. We propose that this intermediate chaotic but non-ergodic regime characterizes mesoscopic quantum dot and diamond defect systems, as we see no numerical tendency towards conventional thermalization with a finite relaxation time.

Extraction of radiographic findings from unstructured thoracoabdominal computed tomography reports using convolutional neural network based natural language processing.

BACKGROUND: Heart failure (HF) is a major cause of morbidity and mortality. However, much of the clinical data is unstructured in the form of radiology reports, while the process of data collection and curation is arduous and time-consuming. PURPOSE: We utilized a machine learning (ML)-based natural language processing (NLP) approach to extract clinical terms from unstructured radiology reports. Additionally, we investigate the prognostic value of the extracted data in predicting all-cause mortality (ACM) in HF patients. MATERIALS AND METHODS: This observational cohort study utilized 122,025 thoracoabdominal computed tomography (CT) reports from 11,808 HF patients obtained between 2008 and 2018. 1,560 CT reports were manually annotated for the presence or absence of 14 radiographic findings, in addition to age and gender. Thereafter, a Convolutional Neural Network (CNN) was trained, validated and tested to determine the presence or absence of these features. Further, the ability of CNN to predict ACM was evaluated using Cox regression analysis on the extracted features. RESULTS: 11,808 CT reports were analyzed from 11,808 patients (mean age 72.8 ± 14.8 years; 52.7% (6,217/11,808) male) from whom 3,107 died during the 10.6-year follow-up. The CNN demonstrated excellent accuracy for retrieval of the 14 radiographic findings with area-under-the-curve (AUC) ranging between 0.83-1.00 (F1 score 0.84-0.97). Cox model showed the time-dependent AUC for predicting ACM was 0.747 (95% confidence interval [CI] of 0.704-0.790) at 30 days. CONCLUSION: An ML-based NLP approach to unstructured CT reports demonstrates excellent accuracy for the extraction of predetermined radiographic findings, and provides prognostic value in HF patients.

Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry.

AIMS: Symptom-based pretest probability scores that estimate the likelihood of obstructive coronary artery disease (CAD) in stable chest pain have moderate accuracy. We sought to develop a machine learning (ML) model, utilizing clinical factors and the coronary artery calcium score (CACS), to predict the presence of obstructive CAD on coronary computed tomography angiography (CCTA). METHODS AND RESULTS: The study screened 35 281 participants enrolled in the CONFIRM registry, who underwent ≥64 detector row CCTA evaluation because of either suspected or previously established CAD. A boosted ensemble algorithm (XGBoost) was used, with data split into a training set (80%) on which 10-fold cross-validation was done and a test set (20%). Performance was assessed of the (1) ML model (using 25 clinical and demographic features), (2) ML + CACS, (3) CAD consortium clinical score, (4) CAD consortium clinical score + CACS, and (5) updated Diamond-Forrester (UDF) score. The study population comprised of 13 054 patients, of whom 2380 (18.2%) had obstructive CAD (≥50% stenosis). Machine learning with CACS produced the best performance [area under the curve (AUC) of 0.881] compared with ML alone (AUC of 0.773), CAD consortium clinical score (AUC of 0.734), and with CACS (AUC of 0.866) and UDF (AUC of 0.682), P < 0.05 for all comparisons. CACS, age, and gender were the highest ranking features. CONCLUSION: A ML model incorporating clinical features in addition to CACS can accurately estimate the pretest likelihood of obstructive CAD on CCTA. In clinical practice, the utilization of such an approach could improve risk stratification and help guide downstream management.

Identification and Quantification of Cardiovascular Structures From CCTA: An End-to-End, Rapid, Pixel-Wise, Deep-Learning Method.

OBJECTIVES: This study designed and evaluated an end-to-end deep learning solution for cardiac segmentation and quantification. BACKGROUND: Segmentation of cardiac structures from coronary computed tomography angiography (CCTA) images is laborious. We designed an end-to-end deep-learning solution. METHODS: Scans were obtained from multicenter registries of 166 patients who underwent clinically indicated CCTA. Left ventricular volume (LVV) and right ventricular volume (RVV), left atrial volume (LAV) and right atrial volume (RAV), and left ventricular myocardial mass (LVM) were manually annotated as ground truth. A U-Net-inspired, deep-learning model was trained, validated, and tested in a 70:20:10 split. RESULTS: Mean age was 61.1 ± 8.4 years, and 49% were women. A combined overall median Dice score of 0.9246 (interquartile range: 0.8870 to 0.9475) was achieved. The median Dice scores for LVV, RVV, LAV, RAV, and LVM were 0.938 (interquartile range: 0.887 to 0.958), 0.927 (interquartile range: 0.916 to 0.946), 0.934 (interquartile range: 0.899 to 0.950), 0.915 (interquartile range: 0.890 to 0.920), and 0.920 (interquartile range: 0.811 to 0.944), respectively. Model prediction correlated and agreed well with manual annotation for LVV (r = 0.98), RVV (r = 0.97), LAV (r = 0.78), RAV (r = 0.97), and LVM (r = 0.94) (p < 0.05 for all). Mean difference and limits of agreement for LVV, RVV, LAV, RAV, and LVM were 1.20 ml (95% CI: -7.12 to 9.51), -0.78 ml (95% CI: -10.08 to 8.52), -3.75 ml (95% CI: -21.53 to 14.03), 0.97 ml (95% CI: -6.14 to 8.09), and 6.41 g (95% CI: -8.71 to 21.52), respectively. CONCLUSIONS: A deep-learning model rapidly segmented and quantified cardiac structures. This was done with high accuracy on a pixel level, with good agreement with manual annotation, facilitating its expansion into areas of research and clinical import.

Floquet-Engineering Counterdiabatic Protocols in Quantum Many-Body Systems.

Counterdiabatic (CD) driving presents a way of generating adiabatic dynamics at an arbitrary pace, where excitations due to nonadiabaticity are exactly compensated by adding an auxiliary driving term to the Hamiltonian. While this CD term is theoretically known and given by the adiabatic gauge potential, obtaining and implementing this potential in many-body systems is a formidable task, requiring knowledge of the spectral properties of the instantaneous Hamiltonians and control of highly nonlocal multibody interactions. We show how an approximate gauge potential can be systematically built up as a series of nested commutators, remaining well defined in the thermodynamic limit. Furthermore, the resulting CD driving protocols can be realized up to arbitrary order without leaving the available control space using tools from periodically driven (Floquet) systems. This is illustrated on few- and many-body quantum systems, where the resulting Floquet protocols significantly suppress dissipation and provide a drastic increase in fidelity.