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In patients with out-of-hospital cardiac arrest (OHCA) who attain return of spontaneous circulation (ROSC), rearrest while in the prehospital setting represents a significant barrier to survival. To date, there are limited data to guide prehospital emergency medical services (EMS) management immediately following successful resuscitation resulting in ROSC and prior to handoff in the emergency department. Post-ROSC care encompasses a multifaceted approach including hemodynamic optimization, airway management, oxygenation, and ventilation. We sought to develop an evidenced-based, goal-directed bundle of care targeting specified vital parameters in the immediate post-ROSC period, with the goal of decreasing the incidence of rearrest and improving survival outcomes. Here, we describe the rationale and development of this goal-directed bundle of care, which will be adopted by several EMS agencies within California. We convened a group of EMS experts, including EMS Medical Directors, quality improvement officers, data managers, educators, EMS clinicians, emergency medicine clinicians, and resuscitation researchers to develop a goal-directed bundle of care to be applied in the field during the period immediately following ROSC. This care bundle includes guidance for prehospital personnel on recognition of impending rearrest, hemodynamic optimization, ventilatory strategies, airway management, and diagnosis of underlying causes prior to the initiation of transport.

OBJECTIVES: Despite the significant disease burden due to cardiac arrest, there is a relative paucity of randomized controlled trials (RCTs) to inform definitive management. We aimed to evaluate the current scope of cardiac arrest RCTs published between 2015 and 2022. METHODS: We conducted a search in October 2023 of MEDLINE, Embase, and Web of Science for cardiac arrest RCTs. We included trials published between 2015 and 2022 enrolling human subjects suffering from non-traumatic cardiac arrest. Descriptive statistics were reported and the Mann Kendall test was used to evaluate for temporal trends in the number of trials published annually. RESULTS: We identified 1764 unique publications, 87 RCTs were included after title/abstract and full-text review. We found no significant increase in trials published annually (eight in 2015 and 16 in 2022, p = 1.0). Geographic analysis of study centers found 31 countries represented; Denmark (n = 13, 15%) and the United States (n = 9, 10%) conducted the majority of trials. Nearly all trials included adults (n = 84, 97%) and few included children (n = 9, 10%). The majority of trials focused on out-of-hospital cardiac arrest (n = 62, 71%). Thirty-eight (44%) trials used an intervention characterized as a process improvement; 28 (32%) interventions were characterized as a drug and 20 (23%) as a device. Interventions were implemented with similar frequency in the prehospital (33%) and intensive care unit (38%) setting, as well as similarly between the intra-arrest (53%) and post-arrest (46%) periods. Twenty (27%) trials selected a primary outcome of survival at ≥ 28 days. CONCLUSIONS: Publication of cardiac arrest RCTs remained constant between 2015 and 2022. We identified significant gaps including a lack of trials examining in-hospital cardiac arrest and pediatric patients.

BACKGROUND: Artificial intelligence (AI) has transformative potential to support prehospital clinicians, emergency physicians, and trauma surgeons in acute traumatic injury care. This scoping review examines the literature evaluating AI models using prehospital features to support early traumatic injury care. METHODS: We conducted a systematic search in August 2023 of PubMed, Embase, and Web of Science. Two independent reviewers screened titles/abstracts, with a third reviewer for adjudication, followed by a full-text analysis. We included original research and conference presentations evaluating AI models-machine learning (ML), deep learning (DL), and natural language processing (NLP)-that used prehospital features or features available immediately upon emergency department arrival. Review articles were excluded. The same investigators extracted data and systematically categorized outcomes to ensure consistency and transparency. We calculated kappa for interrater reliability and descriptive statistics. RESULTS: We identified 1050 unique publications, with 49 meeting inclusion criteria after title and abstract review (kappa 0.58) and full-text review. Publications increased annually from 2 in 2007 to 10 in 2022. Geographic analysis revealed a 61% focus on data from the United States. Studies were predominantly retrospective (88%), used local (45%) or national level (41%) data, focused on adults only (59%) or did not specify adults or pediatrics (27%), and 57% encompassed both blunt and penetrating injury mechanisms. The majority used machine learning (88%) alone or in conjunction with DL or NLP, and the top three algorithms used were support vector machine, logistic regression, and random forest. The most common study objectives were to predict the need for critical care and life-saving interventions (29%), assist in triage (22%), and predict survival (20%). CONCLUSIONS: A small but growing body of literature described AI models based on prehospital features that may support decisions made by dispatchers, Emergency Medical Services clinicians, and trauma teams in early traumatic injury care.

The fundamental trade-off between current and future reproduction has long been considered to result in a tendency for species that can grow large to begin reproduction at a larger size. Due to the prolonged time required to reach maturity, estimates of tree maturation size remain very rare and we lack a global view on the generality and the shape of this trade-off. Using seed production from five continents, we estimate tree maturation sizes for 486 tree species spanning tropical to boreal climates. Results show that a species' maturation size increases with maximum size, but in a non-proportional way: the largest species begin reproduction at smaller sizes than would be expected if maturation were simply proportional to maximum size. Furthermore, the decrease in relative maturation size is steepest in cold climates. These findings on maturation size drivers are key to accurately represent forests' responses to disturbance and climate change.

PurposeMetformin use has been associated with a decreased risk of age-related macular degeneration (AMD) progression in observational studies. We aimed to evaluate the efficacy of oral metformin for slowing geographic atrophy (GA) progression.DesignParallel-group, multicenter, randomized phase II clinical trial.ParticipantsParticipants aged ≥ 55 years without diabetes who had GA from atrophic AMD in ≥ 1 eye.MethodsWe enrolled participants across 12 clinical centers and randomized participants in a 1:1 ratio to receive oral metformin (2000 mg daily) or observation for 18 months. Fundus autofluorescence imaging was obtained at baseline and every 6 months.Main outcome measuresThe primary efficacy endpoint was the annualized enlargement rate of the square root-transformed GA area. Secondary endpoints included best-corrected visual acuity (BCVA) and low luminance visual acuity (LLVA) at each visit.ResultsOf 66 enrolled participants, 34 (57 eyes) were randomized to the observation group and 32 (53 eyes) were randomized to the treatment group. The median follow-up duration was 13.9 and 12.6 months in the observation and metformin groups, respectively. The mean ± standard error annualized enlargement rate of square root transformed GA area was 0.35 ± 0.04 mm/year in the observation group and 0.42 ± 0.04 mm/year in the treatment group (risk difference = 0.07 mm/year, 95% confidence interval = -0.05 to 0.18 mm/year; P = 0.26). The mean ± standard error decline in BCVA was 4.8 ± 1.7 letters/year in the observation group and 3.4 ± 1.1 letters/year in the treatment group (P = 0.56). The mean ± standard error decline in LLVA was 7.3 ± 2.5 letters/year in the observation group and 0.8 ± 2.2 letters/year in the treatment group (P = 0.06). Fourteen participants in the metformin group experienced nonserious adverse events related to metformin, with gastrointestinal side effects as the most common. No serious adverse events were attributed to metformin.ConclusionsThe results of this trial as conducted do not support oral metformin having effects on reducing the progression of GA. Additional placebo-controlled trials are needed to explore the role of metformin for AMD, especially for earlier stages of the disease.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Abstract Background Assess the intake of carbohydrate quality and their association with cardiovascular risk factors among diabetes subtypes. Methods Participants of the German Diabetes Study (GDS) (recent-onset diabetes (n = 487) and 5-years thereafter (n = 209)) were allocated into severe autoimmune diabetes (SAID, 35%), severe insulin-deficient diabetes (SIDD, 3%), severe insulin-resistant diabetes (SIRD, 5%), mild obesity-related diabetes (MOD, 28%), and mild age-related diabetes (MARD, 29%). Dietary glycemic index (GI), glycemic load (GL), and intake of higher- (≥ 55) and low-GI (

Abstract Background The ability to non-invasively measure left atrial pressure would facilitate the identification of patients at risk of pulmonary congestion and guide proactive heart failure care. Wearable cardiac monitors, which record single-lead electrocardiogram data, provide information that can be leveraged to infer left atrial pressures. Methods We developed a deep neural network using single-lead electrocardiogram data to determine when the left atrial pressure is elevated. The model was developed and internally evaluated using a cohort of 6739 samples from the Massachusetts General Hospital (MGH) and externally validated on a cohort of 4620 samples from a second institution. We then evaluated model on patch-monitor electrocardiographic data on a small prospective cohort. Results The model achieves an area under the receiver operating characteristic curve of 0.80 for detecting elevated left atrial pressures on an internal holdout dataset from MGH and 0.76 on an external validation set from a second institution. A further prospective dataset was obtained using single-lead electrocardiogram data with a patch-monitor from patients who underwent right heart catheterization at MGH. Evaluation of the model on this dataset yielded an area under the receiver operating characteristic curve of 0.875 for identifying elevated left atrial pressures for electrocardiogram signals acquired close to the time of the right heart catheterization procedure. Conclusions These results demonstrate the utility and the potential of ambulatory cardiac hemodynamic monitoring with electrocardiogram patch-monitors.

BACKGROUND: Artificial intelligence (AI) has demonstrated significant potential in supporting emergency medical services personnel during out-of-hospital cardiac arrest (OHCA) care; however, the extent of research evaluating this topic is unknown. This scoping review examines the breadth of literature on the application of AI in early OHCA care. METHODS: We conducted a search of PubMed®, Embase, and Web of Science in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. Articles focused on non-traumatic OHCA and published prior to January 18th, 2023 were included. Studies were excluded if they did not use an AI intervention (including machine learning, deep learning, or natural language processing), or did not utilize data from the prehospital phase of care. RESULTS: Of 173 unique articles identified, 54 (31%) were included after screening. Of these studies, 15 (28%) were from the year 2022 and with an increasing trend annually starting in 2019. The majority were carried out by multinational collaborations (20/54, 38%) with additional studies from the United States (10/54, 19%), Korea (5/54, 10%), and Spain (3/54, 6%). Studies were classified into three major categories including ECG waveform classification and outcome prediction (24/54, 44%), early dispatch-level detection and outcome prediction (7/54, 13%), return of spontaneous circulation and survival outcome prediction (15/54, 20%), and other (9/54, 16%). All but one study had a retrospective design. CONCLUSIONS: A small but growing body of literature exists describing the use of AI to augment early OHCA care.

Introduction: Hyaluronan, or hyaluronic acid (HA), is a naturally occurring glycosaminoglycan present in the skin, joints, and eyes that provides hydration, lubrication, protection, and other important benefits. HA in dermatology is commonly discussed in the context of its anti-ageing properties. However, both pre-clinical and clinical studies have shown numerous applications of HA, low-molecular-weight (LMW) HA, and hybrid LMW/high-molecular-weight HA in dermatology. LMW-HA exhibits antioxidant, anti-tumour, and angiogenic properties, and given its size, an ability to fully penetrate the skin. Aims: The purpose of this review was to explore the current science and utility of LMW-HA in clinical dermatology and provide an update on its use. Methods: A PubMed search from 2003–2023 on LMW-HA was conducted to evaluate LMW-HA’s utility in clinical dermatology. Results: Identified applications of LMW-HA in medical dermatology included treatment of acute and chronic wounds, rosacea, scars, and seborrhoeic dermatitis. Cosmetic applications of LMW and hybrid HA included treatment of skin ageing, enlarged pores, and skin laxity, as well as enhancement of skin hydration. Topical LMW-HA administration promoted healing after cosmetic procedures, chemical peels, and ingrown toenail surgery. Studies of topical and oral LMW-HA demonstrated adequate safety, and newer techniques of administration, such as needleless jet injection, are available. Conclusion: Exploration and understanding of the properties and benefits of LMW-HA are key to translating its usage in the clinical setting. Basic scientists and dermatologists have achieved substantial progress over the past two decades, and several applications of LMW-HA in dermatology were identified. Additional advantages of LMW-HA are worth exploring.

The innovative trend of “as a service” due to digital development and the rise of issues such as air pollution and traffic congestion led to the emergence of Mobility as a Service (MaaS) in the transportation sector. Companies and governments are experimenting to create a sustainable and efficient transportation future with MaaS. However, MaaS realization and business success from MaaS are still in their growing phase, making this study particularly relevant and timely. This study aims to identify the attributes of users’ selection of integrated mobility app services and the MaaS attributes that affect the behavioral intention to use through the mediation of perceived usefulness and perceived ease of use. This study marked four selection attributes—habit-congruence, information accuracy, relative advantage on efficiency, and IT system quality—for the integrated mobility app service, and 315 actual users of integrated mobility apps in Republic of Korea were sampled and analyzed. In terms of influence, information accuracy, relative advantage on efficiency, and habit-congruence significantly impacted perceived usefulness, in which habit-congruence had the most significant impact on perceived ease of use. In addition, habit-congruence and information accuracy were found to positively affect the behavioral intention to use, mediated by perceived usefulness and perceived ease. We also found that IT system quality was not a user selection attribute where this study was conducted. By providing empirical findings, this study can give management guidelines to companies and researchers in developing integrated mobility app service strategies to increase the number of users and maintain long-term customer relationships.