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Influenza vaccination plays a crucial role in reducing morbidity and mortality from influenza. However, its effectiveness varies due to multiple factors. Reliable point systems combining age, sex, BMI, vaccination history, and other baseline characteristics could aid in making evidence-based decisions regarding influenza vaccination. Using human vaccination cohort data from the University of Georgia (UGA) over multiple influenza seasons, we developed two point systems: the Simple-Test score (STS) and the No-Test score (NTS). These scores predict vaccine-elicited antibody responses measured by hemagglutination inhibition (HAI) titers. Data from four influenza seasons (2016–2017 to 2019–2020) were used for model development and validation. The STS and NTS demonstrated good performance in discriminating between predicted lower-, moderate-, and higher-response groups. The AUC values for the STS were 0.943 for derivation and 0.841, 0.936, and 0.796 from the validation cohorts for 2016–2017, 2018–2019, and 2019–2020, respectively. Age, race, BMI, baseline HAI titers, and vaccination history significantly influenced the point system's performance. The point system showed robustness across age groups (teenagers, adults, and elderly). The AUC values for the NTS were 0.913 for derivation and 0.658 to 0.875 for validation datasets. We successfully developed and validated two practical point systems to predict individual-level influenza vaccine-elicited antibody responses. These systems could facilitate personalized vaccination recommendations, policymaking, and resource allocation in influenza vaccination programs. The proposed point system is also a valuable tool for targeting populations that are likely to benefit most from influenza vaccination.

Recent breakthroughs in artificial intelligence (AI) language models have elevated the vision of using conversational AI support for mental health, with a growing body of literature indicating varying degrees of efficacy. In this paper, we ask when, in therapy, it will be easier to replace humans and, conversely, in what instances, human connection will still be more valued. We suggest that empathy lies at the heart of the answer to this question. First, we define different aspects of empathy and outline the potential empathic capabilities of humans versus AI. Next, we consider what determines when these aspects are needed most in therapy, both from the perspective of therapeutic methodology and from the perspective of patient objectives. Ultimately, our goal is to prompt further investigation and dialogue, urging both practitioners and scholars engaged in AI-mediated therapy to keep these questions and considerations in mind when investigating AI implementation in mental health.

Physicians make critical time-constrained decisions every day. Clinical predictive models can help physicians and administrators make decisions by forecasting clinical and operational events. Existing structured data-based clinical predictive models have limited use in everyday practice owing to complexity in data processing, as well as model development and deployment 1 – 3 . Here we show that unstructured clinical notes from the electronic health record can enable the training of clinical language models, which can be used as all-purpose clinical predictive engines with low-resistance development and deployment. Our approach leverages recent advances in natural language processing 4 , 5 to train a large language model for medical language (NYUTron) and subsequently fine-tune it across a wide range of clinical and operational predictive tasks. We evaluated our approach within our health system for five such tasks: 30-day all-cause readmission prediction, in-hospital mortality prediction, comorbidity index prediction, length of stay prediction, and insurance denial prediction. We show that NYUTron has an area under the curve (AUC) of 78.7–94.9%, with an improvement of 5.36–14.7% in the AUC compared with traditional models. We additionally demonstrate the benefits of pretraining with clinical text, the potential for increasing generalizability to different sites through fine-tuning and the full deployment of our system in a prospective, single-arm trial. These results show the potential for using clinical language models in medicine to read alongside physicians and provide guidance at the point of care. A clinical language model trained on unstructured clinical notes from the electronic health record enhances prediction of clinical and operational events.

Although the importance of edaphic factors and habitat structure for plant growth and survival is known, both are often neglected in favor of climatic drivers when investigating the spatial patterns of plant species and diversity. Yet, especially in mountain ecosystems with complex topography, missing edaphic and habitat components may be detrimental for a sound understanding of biodiversity distribution. Here, we compare the relative importance of climate, soil and land cover variables when predicting the distributions of 2,616 vascular plant species in the European Alps, representing approximately two-thirds of all European flora. Using presence-only data, we built point-process models (PPMs) to relate species observations to different combinations of covariates. We evaluated the PPMs through block cross-validations and assessed the independent contributions of climate, soil, and land cover covariates to predict plant species distributions using an innovative predictive partitioning approach. We found climate to be the most influential driver of spatial patterns in plant species with a relative influence of ~58.5% across all species, with decreasing importance from low to high elevations. Soil (~20.1%) and land cover (~21.4%), overall, were less influential than climate, but increased in importance along the elevation gradient. Furthermore, land cover showed strong local effects in lowlands, while the contribution of soil stabilized at mid-elevations. The decreasing influence of climate with elevation is explained by increasing endemism, and the fact that climate becomes more homogeneous as habitat diversity declines at higher altitudes. In contrast, soil predictors were found to follow the opposite trend. Additionally, at low elevations, human-mediated land cover effects appear to reduce the importance of climate predictors. We conclude that soil and land cover are, like climate, principal drivers of plant species distribution in the European Alps. While disentangling their effects remains a challenge, future studies can benefit markedly by including soil and land cover effects when predicting species distributions.

Maximizing the efficiency of photovoltaic (PV) systems relies heavily on employing efficient maximum power point tracking (MPPT) algorithms. This research focuses on the advancement of enhanced MPPT algorithms capable of achieving the maximum power point (MPP) under different climatic profiles. This paper proposes an adapted perturb and observe-based model predictive control (APO-MPC) strategy to validate the effectiveness of PV systems under three climatic situations. The APO algorithm incorporates variable step sizes to compute reference currents to reduce oscillations while maintaining a steady state in output power. The APO-MPC efficiently tracks and stabilizes output power by predicting future states using reference current and minimizing the cost function. This eliminated the necessity for expensive sensing and communication equipment and networks designed for directly measuring variations in solar irradiation. The computational burden of an algorithm is reduced using a simplified mathematical model of a boost converter and a one-step prediction approach. The PV panel and boost converter are modeled to get appropriate parameters for implementing the proposed algorithm. The system undergoes simulations using the MATLAB/Simulink environment, and multiple test cases are conducted under constant, rapid, and linearly changing irradiances. The outcomes demonstrate that the proposed APO-MPC MPPT algorithm outperforms APO, Kalman filter-based MPC (KMF-MPC), and other existing strategies in terms of stability, transient response time, overshoots, steady-state oscillations, and follow of reference trajectory under dynamic weather conditions.

Because of system constraints caused by the external environment and grid faults, the conventional maximum power point tracking (MPPT) and inverter control methods of a PV power generation system cannot achieve optimal power output. They can also lead to misjudgments and poor dynamic performance. To address these issues, this paper proposes a new MPPT method of PV modules based on model predictive control (MPC) and a finite control set model predictive current control (FCS-MPCC) of an inverter. Using the identification model of PV arrays, the module-based MPC controller is designed, and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature. An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors, the optimal voltage vector is selected according to the optimal value function, and the corresponding optimal switching state is applied to power semiconductor devices of the inverter. The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified, and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink. The results show that MPC has better tracking performance under constraints, and the system has faster and more accurate dynamic response and flexibility than conventional PI control.

Standard tools used to diagnose pulmonary edema in acute decompensated heart failure (ADHF), including chest radiography (CXR), lack adequate sensitivity, which may delay appropriate diagnosis and treatment. Point-of-care lung ultrasonography (LUS) may be more accurate than CXR, but no meta-analysis of studies directly comparing the 2 tools was previously available. To compare the accuracy of LUS with the accuracy of CXR in the diagnosis of cardiogenic pulmonary edema in adult patients presenting with dyspnea. A comprehensive search of MEDLINE, Embase, and Cochrane Library databases and the gray literature was performed in May 2018. No language or year limits were applied. Study inclusion criteria were a prospective adult cohort of patients presenting to any clinical setting with dyspnea who underwent both LUS and CXR on initial assessment with imaging results compared with a reference standard ADHF diagnosis by a clinical expert after either a medical record review or a combination of echocardiography findings and brain-type natriuretic peptide criteria. Two reviewers independently assessed the studies for inclusion criteria, and disagreements were resolved with discussion. Reporting adhered to the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy and the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Two authors independently extracted data and assessed the risk of bias using a customized QUADAS-2 tool. The pooled sensitivity and specificity of LUS and CXR were determined using a hierarchical summary receiver operating characteristic approach. The comparative accuracy of LUS and CXR in diagnosing ADHF as measured by the differences between the 2 modalities in pooled sensitivity and specificity. The literature search yielded 1377 nonduplicate titles that were screened, of which 43 articles (3.1%) underwent full-text review. Six studies met the inclusion criteria, representing a total of 1827 patients. Pooled estimates for LUS were 0.88 (95% Cl, 0.75-0.95) for sensitivity and 0.90 (95% Cl, 0.88-0.92) for specificity. Pooled estimates for CXR were 0.73 (95% CI, 0.70-0.76) for sensitivity and 0.90 (95% CI, 0.75-0.97) for specificity. The relative sensitivity ratio of LUS, compared with CXR, was 1.2 (95% CI, 1.08-1.34; P < .001), but no difference was found in specificity between tests (relative specificity ratio, 1.0; 95% CI, 0.90-1.11; P = .96). The findings suggest that LUS is more sensitive than CXR in detecting pulmonary edema in ADHF; LUS should be considered as an adjunct imaging modality in the evaluation of patients with dyspnea at risk of ADHF.

In physiological hemostasis a prompt recruitment of platelets on the vessel damage prevents the bleeding by the rapid formation of a platelet plug. Qualitative and/or quantitative platelet defects promote bleeding, whereas the high residual reactivity of platelets in patients on antiplatelet therapies moves forward thromboembolic complications. The biochemical mechanisms of the different phases of platelet activation - adhesion, shape change, release reaction, and aggregation - have been well delineated, whereas their complete translation into laboratory assays has not been so fulfilled. Laboratory tests of platelet function, such as bleeding time, light transmission platelet aggregation, lumiaggregometry, impedance aggregometry on whole blood, and platelet activation investigated by flow cytometry, are traditionally utilized for diagnosing hemostatic disorders and managing patients with platelet and hemostatic defects, but their use is still limited to specialized laboratories. To date, a point-of-care testing (POCT) dedicated to platelet function, using pertinent devices much simpler to use, has now become available (ie, PFA-100, VerifyNow System, Multiplate Electrode Aggregometry [MEA]). POCT includes new methodologies which may be used in critical clinical settings and also in general laboratories because they are rapid and easy to use, employing whole blood without the necessity of sample processing. Actually, these different platelet methodologies for the evaluation of inherited and acquired bleeding disorders and/or for monitoring antiplatelet therapies are spreading and the study of platelet function is strengthening. In this review, well-tried and innovative platelet function tests and their methodological features and clinical applications are considered.

Neuroimaging is crucial for assessing mass effect in brain-injured patients. Transport to an imaging suite, however, is challenging for critically ill patients. We evaluated the use of a low magnetic field, portable MRI (pMRI) for assessing midline shift (MLS). In this observational study, 0.064 T pMRI exams were performed on stroke patients admitted to the neuroscience intensive care unit at Yale New Haven Hospital. Dichotomous (present or absent) and continuous MLS measurements were obtained on pMRI exams and locally available and accessible standard-of-care imaging exams (CT or MRI). We evaluated the agreement between pMRI and standard-of-care measurements. Additionally, we assessed the relationship between pMRI-based MLS and functional outcome (modified Rankin Scale). A total of 102 patients were included in the final study (48 ischemic stroke; 54 intracranial hemorrhage). There was significant concordance between pMRI and standard-of-care measurements (dichotomous, κ  = 0.87; continuous, ICC  = 0.94). Low-field pMRI identified MLS with a sensitivity of 0.93 and specificity of 0.96. Moreover, pMRI MLS assessments predicted poor clinical outcome at discharge (dichotomous: adjusted OR 7.98, 95% CI 2.07–40.04 , p  = 0.005; continuous: adjusted OR 1.59, 95% CI 1.11–2.49, p  = 0.021). Low-field pMRI may serve as a valuable bedside tool for detecting mass effect.

In this viewpoint, we explore the use of big data analytics and artificial intelligence (AI) and discuss important challenges to their ethical, effective, and equitable use within opioid use disorder (OUD) treatment settings. Applying our collective experiences as OUD policy and treatment experts, we discuss 8 key challenges that OUD treatment services must contend with to make the most of these rapidly evolving technologies: data and algorithmic transparency, clinical validation, new practitioner-technology interfaces, capturing data relevant to improving patient care, understanding and responding to algorithmic outputs, obtaining informed patient consent, navigating mistrust, and addressing digital exclusion and bias. Through this paper, we hope to critically engage clinicians and policy makers on important ethical considerations, clinical implications, and implementation challenges involved in big data analytics and AI deployment in OUD treatment settings.