A lineage perspective on hominin taxonomy and evolution.

An uncritical reliance on the phylogenetic species concept has led paleoanthropologists to become increasingly typological in their delimitation of new species in the hominin fossil record. As a practical matter, this approach identifies species as diagnosably distinct groups of fossils that share a unique suite of morphological characters but, ontologically, a species is a metapopulation lineage segment that extends from initial divergence to eventual extinction or subsequent speciation. Working from first principles of species concept theory, it is clear that a reliance on morphological diagnosabilty will systematically overestimate species diversity in the fossil record; because morphology can evolve within a lineage segment, it follows that early and late populations of the same species can be diagnosably distinct from each other. We suggest that a combination of morphology and chronology provides a more robust test of the single-species null hypothesis than morphology alone.

Towards Non-Invasive and Continuous Blood Pressure Monitoring in Neonatal Intensive Care Using Artificial Intelligence: A Narrative Review.

Preterm birth is a live birth that occurs before 37 completed weeks of pregnancy. Approximately 11% of babies are born preterm annually worldwide. Blood pressure (BP) monitoring is essential for managing the haemodynamic stability of preterm infants and impacts outcomes. However, current methods have many limitations associated, including invasive measurement, inaccuracies, and infection risk. In this narrative review, we find that artificial intelligence (AI) is a promising tool for the continuous measurement of BP in a neonatal cohort, based on data obtained from non-invasive sensors. Our findings highlight key sensing technologies, AI techniques, and model assessment metrics for BP sensing in the neonatal cohort. Moreover, our findings show that non-invasive BP monitoring leveraging AI has shown promise in adult cohorts but has not been broadly explored for neonatal cohorts. We conclude that there is a significant research opportunity in developing an innovative approach to provide a non-invasive alternative to existing continuous BP monitoring methods, which has the potential to improve outcomes for premature babies.

An Exploratory Review on the Potential of Artificial Intelligence for Early Detection of Acute Kidney Injury in Preterm Neonates.

A preterm birth is a live birth that occurs before 37 completed weeks of pregnancy. Approximately 15 million babies are born preterm annually worldwide, indicating a global preterm birth rate of about 11%. Up to 50% of premature neonates in the gestational age (GA) group of <29 weeks' gestation will develop acute kidney injury (AKI) in the neonatal period; this is associated with high mortality and morbidity. There are currently no proven treatments for established AKI, and no effective predictive tool exists. We propose that the development of advanced artificial intelligence algorithms with neural networks can assist clinicians in accurately predicting AKI. Clinicians can use pathology investigations in combination with the non-invasive monitoring of renal tissue oxygenation (rSO2) and renal fractional tissue oxygenation extraction (rFTOE) using near-infrared spectroscopy (NIRS) and the renal resistive index (RRI) to develop an effective prediction algorithm. This algorithm would potentially create a therapeutic window during which the treating clinicians can identify modifiable risk factors and implement the necessary steps to prevent the onset and reduce the duration of AKI.

Mechanisms to enhance racial equity in health care: Developing a model to facilitate translation of the ACCURE intervention.

Background: As medical and public health professional organizations call on researchers and policy makers to address structural racism in health care, guidance on evidence-based interventions to enhance health care equity is needed. The most promising organizational change interventions to reduce racial health disparities use multilevel approaches and are tailored to specific settings. This study examines the Accountability for Cancer Care through Undoing Racism and Equity (ACCURE) intervention, which changed systems of care at two U.S. cancer centers and eliminated the Black-White racial disparity in treatment completion among patients with early-stage breast and lung cancer. Purpose: We aimed to document key characteristics of ACCURE to facilitate translation of the intervention in other care settings. Methods: We conducted semi-structured interviews with participants who were involved in the design and implementation of ACCURE and analyzed their responses to identify the intervention's mechanisms of change and key components. Results: Study participants (n = 18) described transparency and accountability as mechanisms of change that were operationalized through ACCURE's key components. Intervention components were designed to enhance either institutional transparency (e.g., a data system that facilitated real-time reporting of quality metrics disaggregated by patient race) or accountability of the care system to community values and patient needs for minimally biased, tailored communication and support (e.g., nurse navigators with training in antiracism and proactive care protocols). Conclusions: The antiracism principles transparency and accountability may be effective change mechanisms in equity-focused health services interventions. The model presented in this study can guide future research aiming to adapt ACCURE and evaluate the intervention's implementation and effectiveness in new settings and patient populations.

The 2023 wearable photoplethysmography roadmap.

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Machine learning for understanding and predicting neurodevelopmental outcomes in premature infants: a systematic review.

BACKGROUND: Machine learning has been attracting increasing attention for use in healthcare applications, including neonatal medicine. One application for this tool is in understanding and predicting neurodevelopmental outcomes in preterm infants. In this study, we have carried out a systematic review to identify findings and challenges to date. METHODS: This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Four databases were searched in February 2022, with articles then screened in a non-blinded manner by two authors. RESULTS: The literature search returned 278 studies, with 11 meeting the eligibility criteria for inclusion. Convolutional neural networks were the most common machine learning approach, with most studies seeking to predict neurodevelopmental outcomes from images and connectomes describing brain structure and function. Studies to date also sought to identify features predictive of outcomes; however, results varied greatly. CONCLUSIONS: Initial studies in this field have achieved promising results; however, many machine learning techniques remain to be explored, and the consensus is yet to be reached on which clinical and brain features are most predictive of neurodevelopmental outcomes. IMPACT: This systematic review looks at the question of whether machine learning can be used to predict and understand neurodevelopmental outcomes in preterm infants. Our review finds that promising initial works have been conducted in this field, but many challenges and opportunities remain. Quality assessment of relevant articles is conducted using the Newcastle-Ottawa Scale. This work identifies challenges that remain and suggests several key directions for future research. To the best of the authors' knowledge, this is the first systematic review to explore this topic.

Examining ACCURE's Nurse Navigation Through an Antiracist Lens: Transparency and Accountability in Cancer Care.

There are persistent disparities in the delivery of cancer treatment, with Black patients receiving fewer of the recommended cancer treatment cycles than their White counterparts on average. To enhance racial equity in cancer care, innovative methods that apply antiracist principles to health promotion interventions are needed. The parent study for the current analysis, the Accountability for Cancer Care through Undoing Racism and Equity (ACCURE) intervention, was a system-change intervention that successfully eliminated the Black-White disparity in cancer treatment completion among patients with early-stage breast and lung cancer. The intervention included specially trained nurse navigators who leveraged real-time data to follow-up with patients during their treatment journeys. Community and academic research partners conducted thematic analysis on all clinical notes (n = 3,251) written by ACCURE navigators after each contact with patients in the specialized navigation arm (n = 162). Analysis was informed by transparency and accountability, principles adapted from the antiracist resource Undoing Racism and determined as barriers to treatment completion through prior research that informed ACCURE. We identified six themes in the navigator notes that demonstrated enhanced accountability of the care system to patient needs. Underlying these themes was a process of enhanced data transparency that allowed navigators to provide tailored patient support. Themes include (1) patient-centered advocacy, (2) addressing system barriers to care, (3) connection to resources, (4) re-engaging patients after lapsed treatment, (5) addressing symptoms and side effects, and (6) emotional support. Future interventions should incorporate transparency and accountability mechanisms and examine the impact on racial equity in cancer care.

Training community-based psychotherapists to maintain a therapeutic alliance: A psychotherapy practice research network study.

The goal of this study was to test the efficacy of training community-based psychotherapists who were part of a practice research network to be more attuned to their patients' experiences of the therapeutic relationship. We were particularly interested in the effect of therapist training on the congruence of alliance ratings with their patients. Forty psychotherapists who treated 117 patients were randomly assigned to receive either no training or training, whose learning objectives were to help therapists to develop and maintain a therapeutic alliance. The training included workshops and ongoing consultations to help the clinician to strengthen the therapeutic relationship with the use of mentalizing, attachment theory, countertransference management, and metacommunication. Therapeutic alliance and well-being outcomes were measured at each of six consecutive early psychotherapy sessions. We used the truth and bias model and response surface analysis within a multilevel modeling context to test hypotheses. There was a significantly faster rate of alliance growth in the training versus the no training condition when the alliance was rated by therapists, but not when rated by patients. Trained therapists experienced greater temporal congruence in alliance ratings with their patients compared to untrained therapists. Patient well-being outcomes improved in a session when trained therapists and their patients agreed in their positive alliance ratings in a previous session. This association not significant among untrained therapists. Training therapists in key interpersonally focused skills may lead them to be better attuned to their patients' experiences of the therapeutic relationship. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Racism Runs Through It: Examining The Sexual And Reproductive Health Experience Of Black Women In The South.

Few studies have illustrated how racism influences Black women's use of reproductive health care services. This article presents findings of a collaborative study conducted by a research team and a reproductive justice organization to understand Black women's concerns with sexual and reproductive health services. The qualitative research was conducted with Black women living in Georgia and North Carolina, using a community-based participatory research approach. Themes were developed from participant accounts that highlight how racism, both structural and individual, influenced their reproductive health care access, utilization, and experience. Structural racism affected participants' finances and led some to forgo care or face barriers to obtaining care. Individual racism resulted in some women electing to receive care only from same-race medical providers. These findings suggest a need for policies and practices that address structural barriers to reproductive health care access and improve the reproductive health experience of Black women.

Effect of an Antiracism Intervention on Racial Disparities in Time to Lung Cancer Surgery.

PURPOSE: Timely lung cancer surgery is a metric of high-quality cancer care and improves survival for early-stage non-small-cell lung cancer. Historically, Black patients experience longer delays to surgery than White patients and have lower survival rates. Antiracism interventions have shown benefits in reducing racial disparities in lung cancer treatment. METHODS: We conducted a secondary analysis of Accountability for Cancer Care through Undoing Racism and Equity, an antiracism prospective pragmatic trial, at five cancer centers to assess the impact on overall timeliness of lung cancer surgery and racial disparities in timely surgery. The intervention consisted of (1) a real-time warning system to identify unmet care milestones, (2) race-specific feedback on lung cancer treatment rates, and (3) patient navigation. The primary outcome was surgery within 8 weeks of diagnosis. Risk ratios (RRs) and 95% CIs were estimated using log-binomial regression and adjusted for clinical and demographic factors. RESULTS: A total of 2,363 patients with stage I and II non-small-cell lung cancer were included in the analyses: intervention (n = 263), retrospective control (n = 1,798), and concurrent control (n = 302). 87.1% of Black patients and 85.4% of White patients in the intervention group (P = .13) received surgery within 8 weeks of diagnosis compared with 58.7% of Black patients and 75.0% of White patients in the retrospective group (P < .01) and 64.9% of Black patients and 73.2% of White patients (P = .29) in the concurrent group. Black patients in the intervention group were more likely to receive timely surgery than Black patients in the retrospective group (RR 1.43; 95% CI, 1.26 to 1.64). White patients in the intervention group also had timelier surgery than White patients in the retrospective group (RR 1.10; 95% CI, 1.02 to 1.18). CONCLUSION: Accountability for Cancer Care through Undoing Racism and Equity is associated with timelier lung cancer surgery and reduction of the racial gap in timely surgery.

African American Women's Experiences with Birth After a Prior Cesarean Section.

OBJECTIVES: Women who have had a cesarean section (C-section) and become pregnant again may choose to have a planned repeat cesarean delivery (RCD) or vaginal birth after a cesarean (VBAC). This study aimed to characterize the pregnancy and birth experiences of African American (AA) women who had a successful VBAC, failed VBAC, or RCD. METHODS: Eligible participants (N = 25) self-identified as AA, had a C-section and a subsequent birth(s) in the past 12 years, and were educated past high school. Each participant was individually interviewed via phone call. The Sort and Sift, Think and Shift method was used to evaluate interview transcripts to minimize researcher bias and emphasize the voices of the participants. RESULTS: The resulting themes included the impact of providers on pregnancy and childbirth satisfaction, the value of autonomy in maternal health decision-making, and the role that racism plays in AA women's birth experiences. Although some participants recalled a positive experience, the presence of limited autonomy, lack of support, and negative experiences with providers indicate that birth after a prior C-section for AA women can be improved. CONCLUSIONS: Providers should address their own racial biases and utilize the shared decision-making approach when their patients decide between a VBAC and RCD to improve patient satisfaction.

Expanding the Reach of an Evidence-Based, System-Level, Racial Equity Intervention: Translating ACCURE to the Maternal Healthcare and Education Systems.

The abundance of literature documenting the impact of racism on health disparities requires additional theoretical, statistical, and conceptual contributions to illustrate how anti-racist interventions can be an important strategy to reduce racial inequities and improve population health. Accountability for Cancer Care through Undoing Racism and Equity (ACCURE) was an NIH-funded intervention that utilized an antiracism lens and community-based participatory research (CBPR) approaches to address Black-White disparities in cancer treatment completion. ACCURE emphasized change at the institutional level of healthcare systems through two primary principles of antiracism organizing: transparency and accountability. ACCURE was successful in eliminating the treatment completion disparity and improved completion rates for breast and lung cancer for all participants in the study. The structural nature of the ACCURE intervention creates an opportunity for applications in other health outcomes, as well as within educational institutions that represent social determinants of health. We are focusing on the maternal healthcare and K-12 education systems in particular because of the dire racial inequities faced by pregnant people and school-aged children. In this article, we hypothesize cross-systems translation of a system-level intervention exploring how key characteristics of ACCURE can be implemented in different institutions. Using core elements of ACCURE (i.e., community partners, milestone tracker, navigator, champion, and racial equity training), we present a framework that extends ACCURE's approach to the maternal healthcare and K-12 school systems. This framework provides practical, evidence-based antiracism strategies that can be applied and evaluated in other systems to address widespread structural inequities.

Central pontine myelinolysis in a patient with methamphetamine abuse.

We present a 48-year-old female patient with history of methamphetamine use who developed ataxia, gait difficulty, slurred speech, left Cranial Nerve 7 palsy and mild alteration in mental status. MRI findings suggest that the cause of her newly developed neurological problems is central pontine myelinolysis (CPM).

A hybrid neural network for continuous and non-invasive estimation of blood pressure from raw electrocardiogram and photoplethysmogram waveforms.

BACKGROUND AND OBJECTIVES: Continuous and non-invasive blood pressure monitoring would revolutionize healthcare. Currently, blood pressure (BP) can only be accurately monitored using obtrusive cuff-based devices or invasive intra-arterial monitoring. In this work, we propose a novel hybrid neural network for the accurate estimation of blood pressure (BP) using only non-invasive electrocardiogram (ECG) and photoplethysmogram (PPG) waveforms as inputs. METHODS: This work proposes a hybrid neural network combines the feature detection abilities of temporal convolutional layers with the strong performance on sequential data offered by long short-term memory layers. Raw electrocardiogram and photoplethysmogram waveforms are concatenated and used as network inputs. The network was developed using the TensorFlow framework. Our scheme is analysed and compared to the literature in terms of well known standards set by the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). RESULTS: Our scheme achieves extremely low mean absolute errors (MAEs) of 4.41 mmHg for SBP, 2.91 mmHg for DBP, and 2.77 mmHg for MAP. A strong level of agreement between our scheme and the gold-standard intra-arterial monitoring is shown through Bland Altman and regression plots. Additionally, the standard for BP devices established by AAMI is met by our scheme. We also achieve a grade of 'A' based on the criteria outlined by the BHS protocol for BP devices. CONCLUSIONS: Our CNN-LSTM network outperforms current state-of-the-art schemes for non-invasive BP measurement from PPG and ECG waveforms. These results provide an effective machine learning approach that could readily be implemented into non-invasive wearable devices for use in continuous clinical and at-home monitoring.

Hybridized neural networks for non-invasive and continuous mortality risk assessment in neonates.

Premature birth is the primary risk factor in neonatal deaths, with the majority of extremely premature babies cared for in neonatal intensive care units (NICUs). Mortality risk prediction in this setting can greatly improve patient outcomes and resource utilization. However, existing schemes often require laborious medical testing and calculation, and are typically only calculated once at admission. In this work, we propose a shallow hybrid neural network for the prediction of mortality risk in 3-day, 7-day, and 14-day risk windows using only birthweight, gestational age, sex, and heart rate (HR) and respiratory rate (RR) information from a 12-h window. As such, this scheme is capable of continuously updating mortality risk assessment, enabling analysis of health trends and responses to treatment. The highest performing scheme was the network that considered mortality risk within 3 days, with this scheme outperforming state-of-the-art works in the literature and achieving an area under the receiver-operator curve (AUROC) of 0.9336 with standard deviation of 0.0337 across 5 folds of cross-validation. As such, we conclude that our proposed scheme could readily be used for continuously-updating mortality risk prediction in NICU environments.

Understanding the Racialized Breastfeeding Experiences Among Black Millennials.

Introduction: The World Health Organization recommends 6 months of exclusive breastfeeding for infants. Racial disparities exist, where only 27.9% of black women exclusively breastfeed at 6 months compared to 45.1% of white mothers. Previous research suggests that these disparities are due to a variety of factors, including poor paid leave policies, racism, and bias, but few studies have looked specifically at the experience of black millennial mothers. Methods: This qualitative study aimed to understand the racialized experiences of breastfeeding among black millennials and whether or not there are factors to mitigate the effects of racism. Three focus groups were conducted (N = 15) with black millennial mothers. Participants were recruited through social media sites, emails to breastfeeding/black maternal health organizations, and local partnerships. Inclusion criteria included self-identification as a black/African American woman, born between 1981 and 1996, and having at least one child 5 years or younger. Results: Five major themes emerged from the analysis as follows: (1) institutional racism and barriers, (2) challenges to motherhood, (3) black experiences, (4) breastfeeding in the millennial age, and (5) hopes for the community. Results showed that black millennial mothers expressed being treated differently and poorly due to race. While participants reported supporting each other through their breastfeeding journey, this was not specifically a strategy to impact racism/bias. Discussion: Results also showed that black millennials feel a desire to succeed in breastfeeding to change the narrative about past generations. Further research should explore differences between the breastfeeding experiences and perceptions of black millennials in comparison to that of previous generations.

Determining respiratory rate from photoplethysmogram and electrocardiogram signals using respiratory quality indices and neural networks.

Continuous and non-invasive respiratory rate (RR) monitoring would significantly improve patient outcomes. Currently, RR is under-recorded in clinical environments and is often measured by manually counting breaths. In this work, we investigate the use of respiratory signal quality quantification and several neural network (NN) structures for improved RR estimation. We extract respiratory modulation signals from the electrocardiogram (ECG) and photoplethysmogram (PPG) signals, and calculate a possible RR from each extracted signal. We develop a straightforward and efficient respiratory quality index (RQI) scheme that determines the quality of each moonddulation-extracted respiration signal. We then develop NNs for the estimation of RR, using estimated RRs and their corresponding quality index as input features. We determine that calculating RQIs for modulation-extracted RRs decreased the mean absolute error (MAE) of our NNs by up to 38.17%. When trained and tested using 60-sec waveform segments, the proposed scheme achieved an MAE of 0.638 breaths per minute. Based on these results, our scheme could be readily implemented into non-invasive wearable devices for continuous RR measurement in many healthcare applications.

'It's like you don't have a roadmap really': using an antiracism framework to analyze patients' encounters in the cancer system.

Background: Cancer patients can experience healthcare system-related challenges during the course of their treatment. Yet, little is known about how these challenges might affect the quality and completion of cancer treatment for all patients, and particularly for patients of color. Accountability for Cancer Care through Undoing Racism and Equity is a multi-component, community-based participatory research intervention to reduce Black-White cancer care disparities. This formative work aimed to understand patients' cancer center experiences, explore racial differences in experiences, and inform systems-level interventions.Methods: Twenty-seven breast and lung cancer patients at two cancer centers participated in focus groups, grouped by race and cancer type. Participants were asked about what they found empowering and disempowering regarding their cancer care experiences. The community-guided analysis used a racial equity approach to identify racial differences in care experiences.Results: For Black and White patients, fear, uncertainty, and incomplete knowledge were disempowering; trust in providers and a sense of control were empowering. Although participants denied differential treatment due to race, analysis revealed implicit Black-White differences in care.Conclusions: Most of the challenges participants faced were related to lack of transparency, such that improvements in communication, particularly two-way communication could greatly improve patients' interaction with the system. Pathways for accountability can also be built into a system that allows patients to find solutions for their problems with the system itself. Participants' insights suggest the need for patient-centered, systems-level interventions to improve care experiences and reduce disparities.

Hepatomegaly and short stature in a 14-year-old with type 1 diabetes mellitus: case report.

BACKGROUND: Mauriac syndrome is a rare consequence of poorly controlled insulin-dependent diabetes, characterized by hepatomegaly, growth failure, delayed onset of puberty, and cushingoid features. Case reports of patients with Mauriac syndrome are found infrequently in the literature given historic improvements in diabetes management due to readily available insulin therapy. METHODS: We describe a case of a 14-year-old girl who presented with acute onset abdominal pain, distention, and orthopnea. RESULTS: She had a history of poorly controlled insulin-dependent diabetes as well as short stature. Abdominal imaging revealed impressive hepatomegaly. Laboratory testing showed markedly elevated triglycerides and cholesterol. Mauriac syndrome was suspected and diagnosed by liver biopsy, which demonstrated significant glycogenic hepatopathy. CONCLUSIONS: This case provides an illustrative example of Mauriac syndrome in a child who did not experience delayed onset of puberty and continued to have regular menses unlike what has been previously described. Furthermore, this case highlights the important consideration for significant dyslipidemia in patients with Mauriac syndrome and discusses the challenges of controlling insulin-dependent diabetes in the adolescent population.