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Generative AI (GAI) tools are increasingly used informally for health, yet evidence from low- and middle-income countries (LMICs) is limited. This study generates early evidence on such health systems from the fifth most populous country: Pakistan. We used a youth-led convergent mixed-methods design among digitally connected urban youth in Pakistan (survey N = 1240, 20 interviews). The primary outcome was any GAI use for health. We fitted multivariable logistic regression models and conducted reflexive thematic analysis. Overall, 69.0% of participants reported using GAI for health. Higher odds of use were observed among women (aOR = 1.57, 95% CI [1.17–2.11], p = 0.003) and youth reporting any mental or physical condition (aOR = 1.82, 95% CI [1.34–2.48], p < .001). Greater trust in AI strongly predicted use (per-level aOR = 4.21, 95% CI [2.98–6.01], p < .001). High confidence using AI (aOR = 1.81, 95% CI [1.11–3.07], p = 0.022), awareness of AI risks (aOR = 1.67, 95% CI [1.20–2.31], p = 0.002), and prior use of other (non-generative) digital health tools (aOR = 4.48, 95% CI [2.59–8.23], p < .001) were also associated with higher likelihood of use. Telemedicine use was significant though weaker in magnitude (aOR = 1.58, 95% CI [1.01–2.54], p = 0.049). Interviews highlighted three themes: (1) access and affordability driving first-line use; (2) emotional safety and informational support, especially for stigmatized concerns; (3) perceived empowerment in interpreting tests, organizing symptoms, and preparing for clinical visits. Given constrained, stigmatizing, and costly services, GAI may function as an adjunct step for health information and emotional support in Pakistan’s health ecosystem.

BackgroundCardiovascular disease (CVD) is a predominant cause of mortality. Pharmacists play an important role in secondary prevention of CVD, however, their role in cardiac rehabilitation is under-reported and services are under-utilised.AimTo explore the role of pharmacists in cardiac rehabilitation, the impact of their interventions on patient outcomes, and prospects of future role development.MethodDatabases searched were PubMed, Embase, Medline, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and PsycINFO from January 2006 to October 2021. Randomised and non-randomised controlled trials were selected if they assessed the role of pharmacists in cardiac rehabilitation. Cochrane risk of bias tool, Joanna Briggs Institute (JBI) Critical Appraisal Tool for Quasi-Experimental Studies and the National Heart, Lung and Blood Institute (NIH) quality assessment tool, were used to assess quality and a narrative synthesis was conducted.ResultsThe search yielded 786 studies, only five met the inclusion criteria. The pharmacist-led interventions included patient education, medication review and reconciliation, and medication adherence encouragement. Four out of the five studies showed that pharmacist-led interventions in cardiac rehabilitation significantly improved patient clinical and non-clinical outcomes. One study showed a statistically significant reduction in low density lipoprotein-cholesterol (LDL-C) levels to optimal target of < 70 mg/dL (80% vs 60%, p = 0.0084). Two studies reported better medication adherence, and two studies showed greater improvement in all domains of health-related quality of life observed in the intervention group.ConclusionPharmacist-led interventions in cardiac rehabilitation could lower CVD risk factors and hence recurrence. Although these findings support pharmacists’ involvement in cardiac rehabilitation, larger intervention studies are needed to evaluate the feasibility of pharmacist-led interventions and their impact on hospital admissions and mortality risk.

In this work, we begin by questioning the existence of a new kind of nonergodic extended phase, namely, the many-body critical (MBC) phase in finite systems of an interacting quasiperiodic system. We find that this phase can be separately detected from the other phases such as the many-body ergodic (ME) and many-body localized (MBL) phases in the model through supervised neural networks made for both binary and multi-class classification tasks, utilizing, rather un-preprocessed, eigenvalue spacings and eigenvector probability densities as input features. Moreover, the output of our trained neural networks can also indicate the critical points separating ME, MBC and MBL phases, which are consistent with the same obtained from other conventional methods. We also employ unsupervised learning techniques, particularly principal component analysis (PCA) of eigenvector probability densities to investigate how this framework, without any training, captures the, rather unknown, many-body phases (ME, MBL and MBC) and single particle phases (delocalized, localized and critical) of the interacting and non-interacting systems, respectively. Our findings reveal that PCA entropy serves as an effective indicator (order parameter) for detecting phase transitions in the single-particle systems. Moreover, this method proves applicable to many-body systems when the data undergoes a suitable pre-processing. Interestingly, when it comes to extraction of critical (correlation length) exponents through a finite size-scaling, we find that for single-particle systems, scaling collapse of neural network outputs is obtained using components of inverse participation ratio (IPR) as input data. Remarkably, we observe identical critical exponents as obtained from scaling collapse of the IPR directly for different single-particle phase transitions.

We study the effects of quasiperiodic Aubry-André (AA) disorder and interactions on a one-dimensional all-band-flat (ABF) diamond chain. We consider the application of disorder in two ways: a symmetric one, where the same disorder is applied to the top and bottom sites of a unit cell, and an antisymmetric one, where the disorder applied to the top and bottom sites are of equal magnitude but with opposite signs. The single-particle wave-packet dynamics for the clean system and when the disorder is applied symmetrically show quantum caging; in the antisymmetric case, the wave-packet spreads over the entire lattice. These results agree with our previous work, where compact localization was observed in the case of the clean system and for symmetrically disordered diamond lattices. In the presence of nearest-neighbour interactions, nonergodic phases are observed in the case of a clean system and symmetrical disorder; at higher disorder strengths, we find an MBL-like phase in the symmetric case. However, many-body non-equilibrium dynamics of the system from carefully engineered initial states exhibit quantum caging. In the antisymmetric case, a nonergodic mixed phase, a thermal phase and an MBL-like phases, respectively, are observed at low, intermediate and high disorder strengths. We observe an absence of caging and initial state dependence (except at the intermediate disorder strength) in the study of non-equilibrium dynamics.

In this work, we map the phase diagrams of one-dimensional quasiperiodic models using artificial neural networks. We observe that the multi-class classifier precisely distinguishes the various phases, namely the delocalized, multifractal, and localized phases, when trained on the eigenstates of the long-range Aubry-André Harper (LRH) model. Additionally, when this trained multi-layer perceptron is fed with the eigenstates of the Aubry-André Harper (AAH) model, it identifies various phases with reasonable accuracy. We examine the resulting phase diagrams produced using a single disorder realization and demonstrate that they are consistent with those obtained from the conventional method of fractal dimension analysis. Interestingly, when the neural network is trained using the eigenstates of the AAH model, the resulting phase diagrams for the LRH model are less exemplary than those previously obtained. Further, we study binary classification by training the neural network on the probability density corresponding to the delocalized and localized eigenstates of the AAH model. We are able to pinpoint the critical transition point by examining the metric ``accuracy\" for the central eigenstate. The effectiveness of the binary classifier in identifying a previously unknown multifractal phase is then evaluated by applying it to the LRH model.

Flat-band lattices hosting compact localized states are highly sensitive to external modulation, and the tailored design of a perturbation to imprint specific features becomes relevant. Here we show that periodic driving in the high-frequency regime transforms the all-flat-band diamond chain into one featuring two tunable quasi-flat bands and a residual flat band pinned at \\(E=0\\). The interplay between lattice geometry and the symmetries of the driven system gives rise to drive-induced tunneling processes that redefine the interference conditions and open a controllable route to escaping Aharonov-Bohm caging. Under driving, the diamond chain effectively acquires the geometry of a dimerized lattice, exhibiting charge oscillations between opposite boundaries. This feature can be exploited to generate two-particle entanglement that is directly accessible experimentally. The resulting drive-engineered quasi-flat bands thus provide a versatile platform for manipulating quantum correlations, revealing a direct link between spectral fine structure and dynamical entanglement.

We numerically study the evolution of spectral correlations in the entanglement Hamiltonian (EH) of non-interacting fermions in the Aubry-André-Harper (AAH) model. We analyze the time evolution of the EH spectrum in a nonequilibrium setting by studying several quantities: spectral distribution, level statistics, entanglement entropy, and spectral form factor (SFF) in the context of the delocalization-localization transition in the AAH model. It is observed that the SFF of the entanglement spectrum in the delocalized phase and at the phase-transition point evolves in three-time intervals. We make a systematic study of the emergence of these three timescales for various initial states and find that the number of time intervals remains three unless the Hamiltonian is tuned in the localized phase or when the initial state is maximally entangled, then there is a featureless time evolution. We find a broad direct correlation between the entanglement entropy and the length of the ramp of the SFF. We also find that in the delocalized phase the spectral correlations are stronger in the center of the spectrum and grow progressively weaker as more and more of the spectrum is considered.

We study the effect of quasiperiodic Aubry-André disorder on the energy spectrum and eigenstates of a one-dimensional all-bands-flat (ABF) diamond chain. The ABF diamond chain possesses three dispersionless flat bands with all the eigenstates compactly localized on two unit cells in the zero disorder limit. The fate of the compact localized states in the presence of the disorder depends on the symmetry of the applied potential. We consider two cases here: a symmetric one, where the same disorder is applied to the top and bottom sites of a unit cell and an antisymmetric one, where the disorder applied to the top and bottom sites are of equal magnitude but with opposite signs. Remarkably, the symmetrically perturbed lattice preserves compact localization, although the degeneracy is lifted. When the lattice is perturbed antisymmetrically, not only is the degeneracy is lifted but compact localization is also destroyed. Fascinatingly, all eigenstates exhibit a multifractal nature below a critical strength of the applied potential. A central band of eigenstates continue to display an extended yet non-ergodic behaviour for arbitrarily large strengths of the potential. All other eigenstates exhibit the familiar Anderson localization above the critical potential strength. We show how the antisymmetric disordered model can be mapped to a \\(4\\) rotated square lattice with nearest and selective next-nearest neighbour hopping and a staggered magnetic field - such models have been shown to exhibit multifractality. Surprisingly, the antisymmetric disorder (with an even number of unit cells) preserves chiral symmetry - we show this by explicitly writing down the chiral operator.

The current situation of the unprecedented COVID-19 pandemic leverages Artificial Intelligence (AI) as an innovative tool for addressing the evolving clinical challenges. An example is utilizing Machine Learning (ML) models-a subfield of AI that take advantage of observational data/Electronic Health Records (EHRs) to support clinical decision-making for COVID-19 cases. This study aimed to evaluate the clinical characteristics and risk factors for COVID-19 patients in the United Arab Emirates utilizing EHRs and ML for survival analysis models. We tested various ML models for survival analysis in this work we trained those models using a different subset of features extracted by several feature selection methods. Finally, the best model was evaluated and interpreted using goodness-of-fit based on calibration curves,Partial Dependence Plots and concordance index. The risk of severe disease increases with elevated levels of C-reactive protein, ferritin, lactate dehydrogenase, Modified Early Warning Score, respiratory rate and troponin. The risk also increases with hypokalemia, oxygen desaturation and lower estimated glomerular filtration rate and hypocalcemia and lymphopenia. Analyzing clinical data using AI models can provide vital information for clinician to measure the risk of morbidity and mortality of COVID-19 patients. Further validation is crucial to implement the model in real clinical settings.