Comparable efficacy of oral bendamustine versus intravenous administration in treating hematologic malignancies.

PURPOSE: The purpose of this study was to analyze potential differences in antitumor efficacy and pharmacokinetics between intravenous (IV) bendamustine and a novel orally administered (PO) bendamustine agent that is utilizing the beneficial properties of superstaturated solid dispersions formulated in nanoparticles. METHODS: Pharmacokinetics of IV versus PO bendamustine were determined by analysis of plasma samples collected from NSG mice treated with either IV or PO bendamustine. Plasma samples were analyzed using liquid chromatography-mass spectrometry following a liquid-liquid extraction to determine peak bendamustine concentration, area under the concentration-time curve, and the half-life in-vivo. In-vitro cytotoxicity of bendamustine against human non-Hodgkin Burkitt's Lymphoma (Raji), multiple myeloma (MM.1s), and B-cell acute lymphoblastic leukemia (RS4;11) cell lines was determined over time using MTS assays. Luciferase-tagged versions of the aforementioned cell lines were used to determine in-vivo bendamustine cytotoxicity of IV versus PO bendamustine at two different doses. RESULTS: Bendamustine at a high dose in-vitro causes cell death. There was no significant difference in antitumor activity between IV and novel PO bendamustine at a physiologically relevant concentration in all three xenograft models. In-vivo pharmacokinetics showed the oral bioavailability of bendamustine in mice to be 51.4%. CONCLUSIONS: The novel oral bendamustine agent tested exhibits good oral bioavailability and systemic exposure for in-vivo antitumor efficacy comparable to IV bendamustine. An oral bendamustine formulation offers exciting clinical potential as an additional method of administration for bendamustine and warrants further evaluation in clinical studies.

Optimising the use of electronic medical records for large scale research in psychiatry.

The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.

multimedia: Multimodal Mediation Analysis of Microbiome Data.

Mediation analysis has emerged as a versatile tool for answering mechanistic questions in microbiome research because it provides a statistical framework for attributing treatment effects to alternative causal pathways. Using a series of linked regression models, this analysis quantifies how complementary data modalities relate to one another and respond to treatments. Despite these advances, the rigid modeling assumptions of existing software often results in users viewing mediation analysis as a black box, not something that can be inspected, critiqued, and refined. We designed the multimedia R package to make advanced mediation analysis techniques accessible to a wide audience, ensuring that all statistical components are easily interpretable and adaptable to specific problem contexts. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, and bootstrap confidence interval construction. We illustrate the package through two case studies. The first re-analyzes a study of the microbiome and metabolome of Inflammatory Bowel Disease patients, uncovering potential mechanistic interactions between the microbiome and disease-associated metabolites, not found in the original study. The second analyzes new data about the influence of mindfulness practice on the microbiome. The mediation analysis identifies a direct effect between a randomized mindfulness intervention and microbiome composition, highlighting shifts in taxa previously associated with depression that cannot be explained by diet or sleep behaviors alone. A gallery of examples and further documentation can be found at https://go.wisc.edu/830110.

Defining acceptable data collection and reuse standards for queer artificial intelligence research in mental health: protocol for the online PARQAIR-MH Delphi study.

INTRODUCTION: For artificial intelligence (AI) to help improve mental healthcare, the design of data-driven technologies needs to be fair, safe, and inclusive. Participatory design can play a critical role in empowering marginalised communities to take an active role in constructing research agendas and outputs. Given the unmet needs of the LGBTQI+ (Lesbian, Gay, Bisexual, Transgender, Queer and Intersex) community in mental healthcare, there is a pressing need for participatory research to include a range of diverse queer perspectives on issues of data collection and use (in routine clinical care as well as for research) as well as AI design. Here we propose a protocol for a Delphi consensus process for the development of PARticipatory Queer AI Research for Mental Health (PARQAIR-MH) practices, aimed at informing digital health practices and policy. METHODS AND ANALYSIS: The development of PARQAIR-MH is comprised of four stages. In stage 1, a review of recent literature and fact-finding consultation with stakeholder organisations will be conducted to define a terms-of-reference for stage 2, the Delphi process. Our Delphi process consists of three rounds, where the first two rounds will iterate and identify items to be included in the final Delphi survey for consensus ratings. Stage 3 consists of consensus meetings to review and aggregate the Delphi survey responses, leading to stage 4 where we will produce a reusable toolkit to facilitate participatory development of future bespoke LGBTQI+-adapted data collection, harmonisation, and use for data-driven AI applications specifically in mental healthcare settings. ETHICS AND DISSEMINATION: PARQAIR-MH aims to deliver a toolkit that will help to ensure that the specific needs of LGBTQI+ communities are accounted for in mental health applications of data-driven technologies. The study is expected to run from June 2024 through January 2025, with the final outputs delivered in mid-2025. Participants in the Delphi process will be recruited by snowball and opportunistic sampling via professional networks and social media (but not by direct approach to healthcare service users, patients, specific clinical services, or via clinicians' caseloads). Participants will not be required to share personal narratives and experiences of healthcare or treatment for any condition. Before agreeing to participate, people will be given information about the issues considered to be in-scope for the Delphi (eg, developing best practices and methods for collecting and harmonising sensitive characteristics data; developing guidelines for data use/reuse) alongside specific risks of unintended harm from participating that can be reasonably anticipated. Outputs will be made available in open-access peer-reviewed publications, blogs, social media, and on a dedicated project website for future reuse.

Management of antipsychotics in primary care: Insights from healthcare professionals and policy makers in the United Kingdom.

INTRODUCTION: Antipsychotic medication is increasingly prescribed to patients with serious mental illness. Patients with serious mental illness often have cardiovascular and metabolic comorbidities, and antipsychotics independently increase the risk of cardiometabolic disease. Despite this, many patients prescribed antipsychotics are discharged to primary care without planned psychiatric review. We explore perceptions of healthcare professionals and managers/directors of policy regarding reasons for increasing prevalence and management of antipsychotics in primary care. METHODS: Qualitative study using semi-structured interviews with 11 general practitioners (GPs), 8 psychiatrists, and 11 managers/directors of policy in the United Kingdom. Data was analysed using thematic analysis. RESULTS: Respondents reported competency gaps that impaired ability to manage patients prescribed antipsychotic medications, arising from inadequate postgraduate training and professional development. GPs lacked confidence to manage antipsychotic medications alone; psychiatrists lacked skills to address cardiometabolic risks and did not perceive this as their role. Communication barriers, lack of integrated care records, limited psychology provision, lowered expectation towards patients with serious mental illness by professionals, and pressure to discharge from hospital resulted in patients in primary care becoming 'trapped' on antipsychotics, inhibiting opportunities to deprescribe. Organisational and contractual barriers between services exacerbate this risk, with socioeconomic deprivation and lack of access to non-pharmacological interventions driving overprescribing. Professionals voiced fears of censure if a catastrophic event occurred after stopping an antipsychotic. Facilitators to overcome these barriers were suggested. CONCLUSIONS: People prescribed antipsychotics experience a fragmented health system and suboptimal care. Several interventions could be taken to improve care for this population, but inadequate availability of non-pharmacological interventions and socioeconomic factors increasing mental distress need policy change to improve outcomes. The role of professionals' fear of medicolegal or regulatory censure inhibiting antipsychotic deprescribing was a new finding in this study.

Comparable Efficacy of Oral Bendamustine versus Intravenous Administration in Treating Hematologic Malignancies.

Purpose: The purpose of this study was to analyze potential differences in antitumor efficacy and pharmacokinetics between intravenous (IV) bendamustine (BEN) and a novel orally administered bendamustine agent (PO) that is utilizing the beneficial properties of superstaturated solid dispersions formulated in nanoparticles. Methods: Pharmacokinetics of IV versus PO BEN were determined by analysis of plasma samples collected from NSG mice treated with either IV or PO BEN. Plasma samples were analyzed using liquid chromatography-mass spectrometry (LC/MS/MS) following a liquid-liquid extraction to determine peak BEN concentration (Cmax), area under the concentration-time curve (AUC) and the half-life (t1/2) in-vivo. in-vitro cytotoxicity of BEN against human non-Hodgkin's Burkitt's Lymphoma (Raji), multiple myeloma (MM.1s), and B-cell acute lymphoblastic leukemia (RS4;11) cell lines was determined over time using MTS assays. Luciferase-tagged versions of the aforementioned cell lines were used to determine in-vivo BEN cytotoxicity of IV versus PO BEN at two different doses. Results: Bendamustine at a high dose in-vitro causes cell death. There was no significant difference in antitumor efficacy between IV and novel PO BEN at a physiologically relevant concentration in all three xenograft models. In-vivo pharmacokinetics showed the oral bioavailability of BEN in mice to be 51.4%. Conclusions: The novel oral BEN agent tested exhibits good oral bioavailability and systemic exposure for in-vivo antitumor efficacy comparable to IV BEN. An oral BEN formulation offers exciting clinical potential as an additional method of administration for bendamustine and warrants further evaluation in clinical studies.

Ethical dilemmas in conducting qualitative, public health research on social media: using a study on Facebook as a case.

AIM: Platforms on social media are increasingly used for public health research. While social media provides an exceptional opportunity to explore communication about public health topics, this practice is not without ethical dilemmas. Our aim was to identify and unfold some of these dilemmas and to suggest possible solutions and ways forward for future research. METHODS: Using our own research within a closed forum for people experiencing suicidal thoughts as a case, we explored certain dilemmas and possible answers relating to whether what is to be researched falls under a public or private social media domain; we investigated avenues for obtaining access to participants in an evolving online environment; how to secure informed consent from participants; and ways of ensuring anonymity. RESULTS: We provide recommendations and reflections that we hope will offer inspiration for researchers embarking on similar social media public health research within and beyond suicide research. CONCLUSIONS: The ethical framework commonly referred to in health research, based on confidentiality, anonymity, informed consent and doing no harm must be adjusted to be relevant for a social media context where technologies and regulations are constantly being altered.

Clinical Prompt Learning With Frozen Language Models.

When the first transformer-based language models were published in the late 2010s, pretraining with general text and then fine-tuning the model on a task-specific dataset often achieved the state-of-the-art performance. However, more recent work suggests that for some tasks, directly prompting the pretrained model matches or surpasses fine-tuning in performance with few or no model parameter updates required. The use of prompts with language models for natural language processing (NLP) tasks is known as prompt learning. We investigated the viability of prompt learning on clinically meaningful decision tasks and directly compared this with more traditional fine-tuning methods. Results show that prompt learning methods were able to match or surpass the performance of traditional fine-tuning with up to 1000 times fewer trainable parameters, less training time, less training data, and lower computation resource requirements. We argue that these characteristics make prompt learning a very desirable alternative to traditional fine-tuning for clinical tasks, where the computational resources of public health providers are limited, and where data can often not be made available or not be used for fine-tuning due to patient privacy concerns. The complementary code to reproduce the experiments presented in this work can be found at https://github.com/NtaylorOX/Public_Clinical_Prompt.

IFN-γ and androgens disrupt mitochondrial function in murine myocytes.

The effect of cytokines on non-traditional immunological targets under conditions of chronic inflammation is an ongoing subject of study. Fatigue is a symptom often associated with autoimmune diseases. Chronic inflammatory response and activated cell-mediated immunity are associated with cardiovascular myopathies which can be driven by muscle weakness and fatigue. Thus, we hypothesize that immune dysfunction-driven changes in myocyte mitochondria may play a critical role in fatigue-related pathogenesis. We show that persistent low-level expression of IFN-γ in designated IFN-γ AU-Rich Element deletion mice (ARE mice) under androgen exposure resulted in mitochondrial and metabolic deficiencies in myocytes from male or castrated ARE mice. Most notably, echocardiography unveiled that low ejection fraction in the left ventricle post-stress correlated with mitochondrial deficiencies, explaining how heart function decreases under stress. We report that inefficiencies and structural changes in mitochondria, with changes to expression of mitochondrial genes, are linked to male-biased fatigue and acute cardiomyopathy under stress. Our work highlights how male androgen hormone backgrounds and active autoimmunity reduce mitochondrial function and the ability to cope with stress and how pharmacological blockade of stress signal protects heart function. These studies provide new insight into the diverse actions of IFN-γ in fatigue, energy metabolism, and autoimmunity. © 2023 The Pathological Society of Great Britain and Ireland. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Surgical Resection of Bilateral Polycystic Ovaries Causing Ureteral Obstruction.

Ovarian cysts in adolescents are typically managed conservatively given the low rate of malignancy and the cysts typically regress over time. We present a case of a 14 year-old female with large bilateral adnexal cysts causing ureteral obstruction which was successfully treated with surgical resection and ensuring maximum preservation of ovarian tissue.

Multiagent Intratumoral Immunotherapy Can Be Effective in A20 Lymphoma Clearance and Generation of Systemic T Cell Immunity.

The use of immunotherapies has shown promise against selective human cancers. Identifying novel combinations of innate and adaptive immune cell-activating agents that can work synergistically to suppress tumor growth and provide additional protection against resistance or recurrence is critical. The A20 murine lymphoma model was used to evaluate the effect of various combination immunotherapies administered intratumorally. We show that single-modality treatment with Poly(I:C) or GM-CSF-secreting allogeneic cells only modestly controls tumor growth, whereas when given together there is an improved benefit, with 50% of animals clearing tumors and surviving long-term. Neither heat nor irradiation of GM-CSF-secreting cells enhanced the response over use of live cells. The use of a TIM-3 inhibitory antibody and an OX40 agonist in combination with Poly(I:C) allowed for improved tumor control, with 90% of animals clearing tumors with or without a combination of GM-CSF-secreting cells. Across all treatment groups, mice rejecting their primary A20 tumors were immune to subsequent challenge with A20, and this longstanding immunity was T-cell dependent. The results herein support the use of combinations of innate and adaptive immune activating agents for immunotherapy against lymphoma and should be investigated in other cancer types.

Pyloric Duplication Cyst in Newborn Male.

An enteric duplication cyst (EDC) is a rare congenital anomaly. Although EDCs can occur at any point throughout the gastrointestinal tract, they are most commonly reported in the ileum and only around 5-7% are of gastroduodenal origin. We report a case of a pyloric duplication cyst in a 3 hour old male with prenatal ultrasound showing a cystic mass. The patient had an abdominal ultrasound after birth that showed a mass with probable trilaminar wall. The diagnosis of pyloric duplication cyst was made in surgery and confirmed with histopathologic examination following resection. The patient is doing well with appropriate weight gain at follow-up appointments.

Maternal heart rate variability patterns associated with maternal hypotension and non-reassuring fetal heart rate patterns following initiation of combined spinal-epidural labor analgesia: a prospective observational trial.

BACKGROUND: We evaluated whether baseline maternal heart rate variability (HRV), including the Analgesia Nociception Index (ANI), is associated with maternal hypotension and fetal heart rate (FHR) abnormalities following combined spinal-epidural (CSE) labor analgesia. METHODS: Laboring women were enrolled in this prospective observational study. The primary endpoint was maternal hypotension. The secondary endpoint was FHR abnormalities within 30 min following CSE analgesia initiated with intrathecal plain bupivacaine 1.0 mg and fentanyl 20 µg. The maternal ANI, electrocardiogram, blood pressure, heart rate, oxygen saturation, and FHR tracings were recorded 15 min before and 30 min after CSE. Parturients were grouped based on presence of hypotension and FHR abnormalities. Patient demographics and HRV metrics were compared. Receiver operating characteristics (ROC) curves were constructed for the prediction of hypotension and FHR abnormalities. RESULTS: No significant intergroup differences were detected in patient characteristics. Several baseline HRV metrics and ANI differed significantly between the normotensive (n = 50) and hypotensive (n = 31) groups and between parturients showing FHR abnormalities (n = 19) and those showing reassuring FHR traces (n = 62). The area under the ROC curve (AUC) for predicting hypotension of the baseline low-frequency (LF)/high-frequency (HF) ratio was 0.677 (95% CI 0.55 to 0.80), and that of the ANI was 0.858 (95% CI 0.78 to 0.94). For predicting non-reassuring FHR patterns, the AUC of the LF/HF ratio was 0.77 (95% CI 0.65 to 0.89), and that of the ANI was 0.833 (95% CI 0.72 to 0.94). CONCLUSIONS: The ANI can predict the propensity for maternal hypotension and non-reassuring FHR patterns following CSE.

Risk of stillbirth and preterm birth among undocumented pregnant migrant women in Denmark: A retrospective case-control study.

AIMS: To examine the associations between undocumented pregnant migrant women and the risk of experiencing stillbirth or preterm birth. METHODS: A retrospective case-control study based on nationwide registers from Statistics Denmark and hospital journals from the seven largest hospital wards in Denmark from 1 January 2011 to 31 December 2018. A total of 882 undocumented pregnant migrant women and 3528 matched controls (both documented migrant and non-migrant women) were included. Logistic regression models were used to estimate the risk of undocumented pregnant migrant women experiencing (a) stillbirth and (b) preterm birth compared with the control group. RESULTS: Of the undocumented pregnant migrant women, 33.3% were EU citizens, 16.2% were applicants for residence and 50.5% had an unknown basis for residence. The mean age of the undocumented pregnant migrant women was 28.4 years, whereas the mean age of women in the control group was 30.9 years. Higher adjusted odds of experiencing stillbirth (aOR 3.50; 95% CI 1.31-9.38) and preterm birth (aOR 1.41; 95% CI 1.04-1.93) were observed among the undocumented pregnant migrant women compared with the control group. The basis of residence was not associated with higher odds of experiencing stillbirth or preterm birth. CONCLUSIONS: We found a higher risk of stillbirth and preterm birth among the undocumented pregnant migrant women than in the control group. Our findings suggest a need to increase the focus on providing access to antenatal care among those women currently excluded from this care.

Integrative analyses of radiation-related genes and biomarkers associated with breast cancer.

OBJECTIVE: In addition to significantly reducing breast cancer recurrence risk, radiotherapy also prolongs patients' lives. However, radiotherapy-related genes and biomarkers still remain poorly understood. The present study aimed to identify radiation-associated genes in breast cancer. MATERIALS AND METHODS: Breast cancer data were downloaded from Gene Expression Omnibus (GEO) and UCSC Xena database. The gene ontology (GO) enrichment and gene set enrichment analysis (GSEA) were performed for annotation and integrated discovery. Protein-protein interaction (PPI) network was constructed by STRING database and hub genes were identified. Then, immunohistochemistry and tissue expression of key genes was analyzed by using the Human Protein Atlas (HPA) and GEPIA database. Genes associated with prognosis were identified by performing univariate cox analysis. RESULTS: We identified 341 differentially expressed genes related to radiotherapy in breast cancer patients. PPI analysis revealed a total of 129 nodes and 516 interactions and identified five hub genes (EGFR, FOS, ESR1, JUN, and IL6). In addition, 11 SDEGs THBS1, SERPINA11, NFIL3, METTL7A, KCTD12, HSPA6, EGR1, DDIT4, CCDC3, C11orf96, and BCL2A1 candidate genes can be used as potential diagnostic markers. The calibration curve and ROC indicate good probability consistencies of 3-years and 5-year survival rates of patients between estimation and observation. CONCLUSIONS: Our findings provide novel insight into the functional characteristics of breast cancer through integrative analysis of GEO data and suggest potential biomarkers and therapeutic targets for breast cancer.

Explainable artificial intelligence for mental health through transparency and interpretability for understandability.

The literature on artificial intelligence (AI) or machine learning (ML) in mental health and psychiatry lacks consensus on what "explainability" means. In the more general XAI (eXplainable AI) literature, there has been some convergence on explainability meaning model-agnostic techniques that augment a complex model (with internal mechanics intractable for human understanding) with a simpler model argued to deliver results that humans can comprehend. Given the differing usage and intended meaning of the term "explainability" in AI and ML, we propose instead to approximate model/algorithm explainability by understandability defined as a function of transparency and interpretability. These concepts are easier to articulate, to "ground" in our understanding of how algorithms and models operate and are used more consistently in the literature. We describe the TIFU (Transparency and Interpretability For Understandability) framework and examine how this applies to the landscape of AI/ML in mental health research. We argue that the need for understandablity is heightened in psychiatry because data describing the syndromes, outcomes, disorders and signs/symptoms possess probabilistic relationships to each other-as do the tentative aetiologies and multifactorial social- and psychological-determinants of disorders. If we develop and deploy AI/ML models, ensuring human understandability of the inputs, processes and outputs of these models is essential to develop trustworthy systems fit for deployment.

Automatic Detection of Cognitive Impairment with Virtual Reality.

Cognitive impairment features in neuropsychiatric conditions and when undiagnosed can have a severe impact on the affected individual's safety and ability to perform daily tasks. Virtual Reality (VR) systems are increasingly being explored for the recognition, diagnosis and treatment of cognitive impairment. In this paper, we describe novel VR-derived measures of cognitive performance and show their correspondence with clinically-validated cognitive performance measures. We use an immersive VR environment called VStore where participants complete a simulated supermarket shopping task. People with psychosis (k=26) and non-patient controls (k=128) participated in the study, spanning ages 20-79 years. The individuals were split into two cohorts, a homogeneous non-patient cohort (k=99 non-patient participants) and a heterogeneous cohort (k=26 patients, k=29 non-patient participants). Participants' spatio-temporal behaviour in VStore is used to extract four features, namely, route optimality score, proportional distance score, execution error score, and hesitation score using the Traveling Salesman Problem and explore-exploit decision mathematics. These extracted features are mapped to seven validated cognitive performance scores, via linear regression models. The most statistically important feature is found to be the hesitation score. When combined with the remaining extracted features, the multiple linear regression model resulted in statistically significant results with R2 = 0.369, F-Stat = 7.158, p(F-Stat) = 0.000128.