Diagnostic performance of artificial intelligence in interpreting thyroid nodules on ultrasound images: a multicenter retrospective study.

Background: Thyroid nodules are commonly identified through ultrasound imaging, which plays a crucial role in the early detection of malignancy. The diagnostic accuracy, however, is significantly influenced by the expertise of radiologists, the quality of equipment, and image acquisition techniques. This variability underscores the critical need for computational tools that support diagnosis. Methods: This retrospective study evaluates an artificial intelligence (AI)-driven system for thyroid nodule assessment, integrating clinical practices from multiple prominent Thai medical centers. We included patients who underwent thyroid ultrasonography complemented by ultrasound-guided fine needle aspiration (FNA) between January 2015 and March 2021. Participants formed a consecutive series, enhancing the study's validity. A comparative analysis was conducted between the AI model's diagnostic performance and that of both an experienced radiologist and a third-year radiology resident, using a dataset of 600 ultrasound images from three distinguished Thai medical institutions, each verified with cytological findings. Results: The AI system demonstrated superior diagnostic performance, with an overall sensitivity of 80% [95% confidence interval (CI): 59.3-93.2%] and specificity of 71.4% (95% CI: 53.7-85.4%). At Siriraj Hospital, the AI achieved a sensitivity of 90.0% (95% CI: 55.5-99.8%), specificity of 100.0% (95% CI: 69.2-100%), positive prediction value (PPV) of 100.0%, negative prediction value (NPV) of 90.9%, and an overall accuracy of 95.0%, indicating the benefits of AI's extensive training across diverse datasets. The experienced radiologist's sensitivity was 40.0% (95% CI: 21.1-61.3%), while the specificity was 80.0% (95% CIs: 63.6-91.6%), respectively, showing that the AI significantly outperformed the radiologist in terms of sensitivity (P=0.043) while maintaining comparable specificity. The inter-observer variability analysis indicated a moderate agreement (K=0.53) between the radiologist and the resident, contrasting with fair agreement (K=0.37/0.33) when each was compared with the AI system. Notably, 95% CIs for these diagnostic indexes highlight the AI system's consistent performance across different settings. Conclusions: The findings advocate for the integration of AI into clinical settings to enhance the diagnostic accuracy of radiologists in assessing thyroid nodules. The AI system, designed as a supportive tool rather than a replacement, promises to revolutionize thyroid nodule diagnosis and management by providing a high level of diagnostic precision.

A graded neonatal mouse model of necrotizing enterocolitis demonstrates that mild enterocolitis is sufficient to activate microglia and increase cerebral cytokine expression.

Background: Necrotizing enterocolitis (NEC) is an inflammatory gastrointestinal process that afflicts approximately 10% of preterm infants born in the United States each year, with a mortality rate of 30%. NEC severity is graded using Bell's classification system, from stage I mild NEC to stage III severe NEC. Over half of NEC survivors present with neurodevelopmental impairment during adolescence, a long-term complication that is poorly understood but can occur even after mild NEC. Although multiple animal models exist, none allow the experimenter to control nor represent the gradient of symptom severities seen in NEC patients. We bridge this knowledge gap by developing a graded murine model of NEC and studying its relationship with neuroinflammation across a range of NEC severities. Methods: Postnatal day 3 (P3) C57BL/6 mice were fed a formula containing different concentrations (0% control, 0.25%, 1%, 2%, and 3%) of dextran sodium sulfate (DSS). P3 mice were fed every 3 hours for 72-hours. We collected data on weight gain and behavior (activity, response, body color) during feeding. At the end of the experiment, we collected tissues (intestine, liver, plasma, brain) for immunohistochemistry, immunofluorescence, and cytokine and chemokine analysis. Results: Throughout NEC induction, mice fed higher concentrations of DSS died sooner, lost weight faster, and became sick or lethargic earlier. Intestinal characteristics (dilation, color, friability) were worse in mice fed with higher DSS concentrations. Histology revealed small intestinal disarray among mice fed all DSS concentrations, while higher DSS concentrations resulted in reduced small intestinal cellular proliferation and increased hepatic and systemic inflammation. In the brain, IL-2, G-CSF, and CXCL1 concentrations increased with higher DSS concentrations. Although the number of neurons and microglia in the CA1 hippocampal region did not differ, microglial branching was significantly reduced in DSS-fed mice. Conclusion: We characterize a novel graded model of NEC that recapitulates the full range of NEC severities. We show that mild NEC is sufficient to initiate neuroinflammation and microglia activation. This model will facilitate studies on the neurodevelopmental effects of NEC.

Comparison of Sales From Vending Machines With 4 Different Food and Beverage Messages: A Randomized Trial.

Importance: Point-of-sale food messaging can encourage healthier purchases, but no studies have directly compared multiple interventions in the field. Objective: To examine which of 4 food and beverage messages would increase healthier vending machine purchases. Design, Setting, and Participants: This randomized trial assessed 13 months (February 1, 2019, to February 29, 2020) of vending sales data from 267 machines and 1065 customer purchase assessments from vending machines on government property in Philadelphia, Pennsylvania. Data analysis was performed from March 5, 2020, to November 8, 2022. Interventions: Study interventions were 4 food and beverage messaging systems: (1) beverage tax posters encouraging healthy choices because of the Philadelphia tax on sweetened drinks; (2) green labels for healthy products; (3) traffic light labels: green (healthy), yellow (moderately healthy), or red (unhealthy); or (4) physical activity equivalent labels (minutes of activity to metabolize product calories). Main Outcomes and Measures: Sales data were analyzed separately for beverages and snacks. The main outcomes analyzed at the transaction level were calories sold and the health status (using traffic light criteria) of each item sold. Additional outcomes were analyzed at the monthly machine level: total units sold, calories sold, and units of each health status sold. The customer purchase assessment outcome was calories purchased per vending trip. Results: Monthly sales data came from 150 beverage and 117 snack vending machines, whereas 1065 customers (558 [52%] male) contributed purchase assessment data. Traffic light labels led to a 30% decrease in the mean monthly number of unhealthy beverages sold (mean ratio [MR], 0.70; 95% CI, 0.55-0.88) compared with beverage tax posters. Physical activity labels led to a 34% (MR, 0.66; 95% CI, 0.51-0.87) reduction in the number of unhealthy beverages sold at the machine level and 35% (MR, 0.65; 95% CI, 0.50-0.86) reduction in mean calories sold. Traffic light labels also led to a 30-calorie reduction (b = -30.46; 95% CI, -49.36 to -11.56) per customer trip in the customer purchase analyses compared to physical activity labels. There were very few significant differences for snack machines. Conclusions and Relevance: In this 13-month randomized trial of 267 vending machines, the traffic light and physical activity labels encouraged healthier beverage purchases, but no change in snack sales, compared with a beverage tax poster. Corporations and governments should consider such labeling approaches to promote healthier beverage choices. Trial Registration: ClinicalTrials.gov Identifier: NCT06260176.

Novel insights into tumorigenesis revealed by molecular analysis of Lynch syndrome cases with multiple colorectal tumors.

Background: Lynch syndrome (LS) is an autosomal dominant multi-organ cancer syndrome with a high lifetime risk of cancer. The number of cumulative colorectal adenomas in LS does not generally exceed ten, and removal of adenomas via routine screening minimizes the cancer burden. However, abnormal phenotypes may mislead initial diagnosis and subsequently cause suboptimal treatment. Aim: Currently, there is no standard guide for the care of multiple colorectal adenomas in LS individuals. We aimed to shed insight into the molecular features and reasons for multiplicity of adenomas in LS patients. Methods: We applied whole exome sequencing on nine adenomas (ten samples) and three assumed primary carcinomas (five samples) of an LS patient developing the tumors during a 21-year follow-up period. We compared the findings to the tumor profiles of two additional LS cases ascertained through colorectal tumor multiplicity, as well as to ten adenomas and 15 carcinomas from 23 unrelated LS patients with no elevated adenoma burden from the same population. As LS associated cancers can arise via several molecular pathways, we also profiled the tumors for CpG Island Methylator Phenotype (CIMP), and LINE-1 methylation. Results: All tumors were microsatellite unstable (MSI), and MSI was present in several samples derived from normal mucosa as well. Interestingly, frequent frameshift variants in RNF43 were shared among substantial number of the tumors of our primary case and the tumors of LS cases with multiple tumors but almost absent in our control LS cases. The RNF43 variants were completely absent in the normal tissue, indicating tumor-associated mutational hotspots. The RNF43 status correlated with the mutational signature SBS96. Contrary to LS tumors from the reference set with no elevated colorectal tumor burden, the somatic variants occurred significantly more frequently at C>T in the CpG context, irrespective of CIMP or LINE-1 status, potentially indicating other, yet unknown methylation-related mechanisms. There were no signs of somatic mosaicism affecting the MMR genes. Somatic variants in APC and CTNNB1 were unique to each tumor. Conclusion: Frequent somatic RNF43 hot spot variants combined with SBS96 signature and increased tendency to DNA methylation may contribute to tumor multiplicity in LS.

Prediction of anemia in real-time using a smartphone camera processing conjunctival images.

Anemia is defined as a low hemoglobin (Hb) concentration and is highly prevalent worldwide. We report on the performance of a smartphone application (app) that records images in RAW format of the palpebral conjunctivae and estimates Hb concentration by relying upon computation of the tissue surface high hue ratio. Images of bilateral conjunctivae were obtained prospectively from a convenience sample of 435 Emergency Department patients using a dedicated smartphone. A previous computer-based and validated derivation data set associating estimated conjunctival Hb (HBc) and the actual laboratory-determined Hb (HBl) was used in deriving Hb estimations using a self-contained mobile app. Accuracy of HBc was 75.4% (95% CI 71.3, 79.4%) for all categories of anemia, and Bland-Altman plot analysis showed a bias of 0.10 and limits of agreement (LOA) of (-4.73, 4.93 g/dL). Analysis of HBc estimation accuracy around different anemia thresholds showed that AUC was maximized at transfusion thresholds of 7 and 9 g/dL which showed AUC values of 0.92 and 0.90 respectively. We found that the app is sufficiently accurate for detecting severe anemia and shows promise as a population-sourced screening platform or as a non-invasive point-of-care anemia classifier.

Agnostic identification of plasma biomarkers for postpartum hemorrhage risk.

BACKGROUND: Postpartum hemorrhage (PPH) is difficult to predict, is associated with significant maternal morbidity, and is the leading cause of maternal mortality worldwide. The identification of maternal biomarkers that can predict increased PPH risk would enhance clinical care and may uncover mechanisms that lead to PPH. OBJECTIVE: This retrospective case-control study employed agnostic proteomic profiling of maternal plasma samples to identify differentially abundant proteins in controls and PPH cases. STUDY DESIGN: Maternal plasma samples were procured from a cohort of >60,000 participants in a single institution's perinatal repository. PPH was defined as a decrease in hematocrit of ≥10% or receipt of transfusion within 24 hours of delivery. PPH cases (N=30) were matched by maternal age and delivery mode (vaginal or cesarean) with controls (N=56). Mass spectrometry was used to identify differentially abundant proteins using integrated peptide peak areas. Statistically significant differences between groups were defined by a p-value of <0.05 after controlling for multiple comparisons. RESULTS: By study design, cases and controls did not differ in race, ethnicity, gestational age at delivery, blood type, or pre-delivery platelet count. Cases had slightly, but significantly lower pre- and post-delivery hematocrit and hemoglobin. Mass spectrometry detected 1140 proteins, including 77 proteins for which relative abundance differed significantly between cases and controls (fold change >1.15, P<0.05). Of these differentially abundant plasma proteins, most had likely liver or placental origins. Gene ontology term analysis mapped to protein clusters involved in responses to wound healing, stress response, and host immune defense. Significantly differentially abundant proteins with the highest fold change (prostaglandin D2 synthase, periostin, and several serine protease inhibitors) did not correlate with pre-delivery hematocrit or hemoglobin, but identified PPH cases with logistic regression modeling revealing good-to-excellent area under the operator receiver characteristic curves (AUROC 0.802-0.874). Incorporating pre-delivery hemoglobin with these candidate proteins further improved identification of PPH cases. CONCLUSION: Agnostic analysis of maternal plasma samples identified differentially abundant proteins in controls and PPH cases. Several of these proteins are known to participate in biologically plausible pathways for PPH risk and have potential value for predicting PPH. These findings identify candidate protein biomarkers for future validation and mechanistic studies.

Impact of High Dose Early Mobilization on Outcomes for Patients with Diabetes: A Secondary Analysis of the TEAM Trial.

RATIONALE: Patients with diabetes represent almost 20% of all ICU admissions and might respond differently to high dose early active mobilization. OBJECTIVES: To assess whether diabetes modified the relationship between the dose of early mobilization on clinical outcomes in the TEAM trial. METHODS: All TEAM trial patients were included. The primary outcome was days alive and out of hospital at day 180. Secondary outcomes included 180-day mortality and long-term functional outcomes at day 180. Logistic and median regression models were used to explore the effect of high dose early mobilization on outcomes by diabetes status. MEASUREMENTS AND MAIN RESULTS: All 741 patients from the original trial were included. Of these, 159 patients (21.4%) had diabetes. Patients with diabetes had a lower number of days alive and out of hospital at day 180 (124 [0-153] vs. 147 [82-164], p = 0.013), and higher 180-day mortality (30% vs. 18%, p = 0.044). In patients receiving high dose early mobilization, days alive and out of hospital at day 180 was 73.0 (0.0 - 144.5) in patients with diabetes and 146.5 (95.8 - 163.0) in patients without diabetes (p for interaction = 0.108). However, in patients with diabetes, high dose early mobilization increased the odds of mortality at 180 days (adjusted odds ratio 3.47; 95% confidence interval [CI], 1.67-7.61, p value for interaction, 0.001). CONCLUSIONS: In this secondary analysis of the TEAM trial, in patients with diabetes, a high dose early mobilization strategy did not significantly decrease the number of days alive and out of hospital at day 180 but it increased 180-day mortality.

Personalized Tablets for Residents in Long-Term Care to Support Recreation and Mitigate Isolation.

OBJECTIVES: There is a digital divide in long-term care homes (LTCHs), with few residents having regular access to internet-connected devices. In this study, we provided long-term care residents with personalized and adapted tablets. We aimed to understand what factors influenced tablet use and the impact of tablet access on opportunities for social connection and recreation. DESIGN: A pragmatic, mixed-methods multicenter, open-label, uncontrolled interventional study with assessment of outcomes at baseline and 3 months. SETTING AND PARTICIPANTS: A total of 58 resident-care partner dyads were recruited across 7 LTCHs in Ontario, Canada. The main inclusion criterion was having a care partner willing to participate, and we excluded residents who already had an internet-connected device. METHODS: Resident demographics, functional status assessments, and recreational engagement were captured using items from the Resident Assessment Instrument/Minimum Data Set. Care partners completed a questionnaire about relational closeness and site leads assessed resident quality of life before and approximately 3 months after tablet distribution. Interviews with 23 care partners and 7 residents post-implementation were completed and analyzed. RESULTS: The median tablet use by participants was 7 minutes (interquartile range 27) per day on average over the study period. Predictors of higher tablet use were younger age, higher cognitive functioning, absence of hearing impairment, and having a care partner who lives farther away. There was no improvement on quantitative measures of quality of life, recreation, or relational closeness. In interviews, participants identified many different opportunities afforded by access to personalized tablets. CONCLUSIONS AND IMPLICATIONS: Some LTCH residents without current access to the internet benefit from being provided a personal tablet and use it in a variety of ways to enrich their lives. There is a critical need to bridge the digital divide for this population.

Comparison of Task-Layered Clinical Orientation to Traditional Orientation in a RN Residency Program.

OBJECTIVE: The purpose of this study was to compare outcomes of using a task-layered clinical orientation when compared with the original patient-layering approach. BACKGROUND: Use of task-layering to orient new graduate nurses to the clinical world of nursing has been theorized to provide a decrease in cognitive load and allow for more streamlined clinical orientation. METHODS: The method of this study was a nonrandomized, comparative design to measure the outcomes of length of orientation, new graduate perceptions about level of confidence/comfort with professional nurse responsibilities/skills, stress, satisfaction, and perceptions about orientation. RESULTS: Analysis revealed no statistical significance between the 2 groups. However, the task-layered clinical orientation group completed orientation earlier than the traditional patient-layered group. CONCLUSIONS: The task-layered approach to clinical orientation provided as good of outcomes as traditional orientation strategy and may result in cost savings due to decrease in total clinical orientation days.

Peptides Are Cardioprotective Drugs of the Future: The Receptor and Signaling Mechanisms of the Cardioprotective Effect of Glucagon-like Peptide-1 Receptor Agonists.

The high mortality rate among patients with acute myocardial infarction (AMI) is one of the main problems of modern cardiology. It is quite obvious that there is an urgent need to create more effective drugs for the treatment of AMI than those currently used in the clinic. Such drugs could be enzyme-resistant peptide analogs of glucagon-like peptide-1 (GLP-1). GLP-1 receptor (GLP1R) agonists can prevent ischemia/reperfusion (I/R) cardiac injury. In addition, chronic administration of GLP1R agonists can alleviate the development of adverse cardiac remodeling in myocardial infarction, hypertension, and diabetes mellitus. GLP1R agonists can protect the heart against oxidative stress and reduce proinflammatory cytokine (IL-1ß, TNF-α, IL-6, and MCP-1) expression in the myocardium. GLP1R stimulation inhibits apoptosis, necroptosis, pyroptosis, and ferroptosis of cardiomyocytes. The activation of the GLP1R augments autophagy and mitophagy in the myocardium. GLP1R agonists downregulate reactive species generation through the activation of Epac and the GLP1R/PI3K/Akt/survivin pathway. The GLP1R, kinases (PKCε, PKA, Akt, AMPK, PI3K, ERK1/2, mTOR, GSK-3ß, PKG, MEK1/2, and MKK3), enzymes (HO-1 and eNOS), transcription factors (STAT3, CREB, Nrf2, and FoxO3), KATP channel opening, and MPT pore closing are involved in the cardioprotective effect of GLP1R agonists.

In Search for Low-Molecular-Weight Ligands of Human Serum Albumin That Affect Its Affinity for Monomeric Amyloid ß Peptide.

An imbalance between production and excretion of amyloid ß peptide (Aß) in the brain tissues of Alzheimer's disease (AD) patients leads to Aß accumulation and the formation of noxious Aß oligomers/plaques. A promising approach to AD prevention is the reduction of free Aß levels by directed enhancement of Aß binding to its natural depot, human serum albumin (HSA). We previously demonstrated the ability of specific low-molecular-weight ligands (LMWLs) in HSA to improve its affinity for Aß. Here we develop this approach through a bioinformatic search for the clinically approved AD-related LMWLs in HSA, followed by classification of the candidates according to the predicted location of their binding sites on the HSA surface, ranking of the candidates, and selective experimental validation of their impact on HSA affinity for Aß. The top 100 candidate LMWLs were classified into five clusters. The specific representatives of the different clusters exhibit dramatically different behavior, with 3- to 13-fold changes in equilibrium dissociation constants for the HSA-Aß40 interaction: prednisone favors HSA-Aß interaction, mefenamic acid shows the opposite effect, and levothyroxine exhibits bidirectional effects. Overall, the LMWLs in HSA chosen here provide a basis for drug repurposing for AD prevention, and for the search of medications promoting AD progression.

The long-term negative impact of childhood stroke on language.

Objectives: This study aims to investigate the long-term language outcome in children with unilateral childhood stroke in comparison to those with perinatal strokes and typically developing individuals and to explore the impact of lesion-specific modifiers. Methods: We examined nine patients with childhood stroke, acquired between 0;2 and 16;1 years (CHILD; 3 female, median = 13.5 years, 6 left-sided), 23 patients with perinatal strokes (PERI; 11 female, median = 12.5 years, 16 left-sided), and 33 age-matched typically developing individuals (CONTROL; 15 female, median = 12.33 years). The language outcome was assessed using age-appropriate tasks of the Potsdam Illinois Test of Psycholinguistic Abilities (P-ITPA) or the Peabody Picture Vocabulary Test (PPVT). For group comparisons, study-specific language z-scores were calculated. Non-verbal intelligence was assessed using the Test of Non-verbal Intelligence (TONI-4), language lateralization with functional MRI, and lesion size with MRI-based volumetry. Results: All four patients with childhood stroke who initially presented with aphasic symptoms recovered from aphasia. Patients with childhood stroke showed significantly lower language scores than those in the control group, but their scores were similar to those of the patients with perinatal stroke, after adjusting for general intelligence (ANCOVA, language z-score CHILD = -0.30, PERI = -0.38, CONTROL = 0.42). Among the patients with childhood stroke, none of the possible modifying factors, including lesion side, correlated significantly with the language outcome. Conclusion: Childhood stroke, regardless of the affected hemisphere, can lead to chronic language deficits, even though affected children show a "full recovery." The rehabilitation of children and adolescents with childhood stroke should address language abilities, even after the usually quick resolution of clear aphasic symptoms.

HPK1 citron homology domain regulates phosphorylation of SLP76 and modulates kinase domain interaction dynamics.

Hematopoietic progenitor kinase 1 (HPK1) is a negative regulator of T-cell receptor signaling and as such is an attractive target for cancer immunotherapy. Although the role of the HPK1 kinase domain (KD) has been extensively characterized, the function of its citron homology domain (CHD) remains elusive. Through a combination of structural, biochemical, and mechanistic studies, we characterize the structure-function of CHD in relationship to KD. Crystallography and hydrogen-deuterium exchange mass spectrometry reveal that CHD adopts a seven-bladed ß-propellor fold that binds to KD. Mutagenesis associated with binding and functional studies show a direct correlation between domain-domain interaction and negative regulation of kinase activity. We further demonstrate that the CHD provides stability to HPK1 protein in cells as well as contributes to the docking of its substrate SLP76. Altogether, this study highlights the importance of the CHD in the direct and indirect regulation of HPK1 function.

Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions.

The explosion of sequence data has allowed the rapid growth of protein language models (pLMs). pLMs have now been employed in many frameworks including variant-effect and peptide-specificity prediction. Traditionally, for protein-protein or peptide-protein interactions (PPIs), corresponding sequences are either co-embedded followed by post-hoc integration or the sequences are concatenated prior to embedding. Interestingly, no method utilizes a language representation of the interaction itself. We developed an interaction LM (iLM), which uses a novel language to represent interactions between protein/peptide sequences. Sliding Window Interaction Grammar (SWING) leverages differences in amino acid properties to generate an interaction vocabulary. This vocabulary is the input into a LM followed by a supervised prediction step where the LM's representations are used as features. SWING was first applied to predicting peptide:MHC (pMHC) interactions. SWING was not only successful at generating Class I and Class II models that have comparable prediction to state-of-the-art approaches, but the unique Mixed Class model was also successful at jointly predicting both classes. Further, the SWING model trained only on Class I alleles was predictive for Class II, a complex prediction task not attempted by any existing approach. For de novo data, using only Class I or Class II data, SWING also accurately predicted Class II pMHC interactions in murine models of SLE (MRL/lpr model) and T1D (NOD model), that were validated experimentally. To further evaluate SWING's generalizability, we tested its ability to predict the disruption of specific protein-protein interactions by missense mutations. Although modern methods like AlphaMissense and ESM1b can predict interfaces and variant effects/pathogenicity per mutation, they are unable to predict interaction-specific disruptions. SWING was successful at accurately predicting the impact of both Mendelian mutations and population variants on PPIs. This is the first generalizable approach that can accurately predict interaction-specific disruptions by missense mutations with only sequence information. Overall, SWING is a first-in-class generalizable zero-shot iLM that learns the language of PPIs.

Gender-based heterogeneity of FAHFAs in trained runners.

Fatty acid esters of hydroxy fatty acid (FAHFA) are anti-diabetic and anti-inflammatory lipokines. Recently FAHFAs were also found to predict cardiorespiratory fitness in a cross-sectional study of recreationally trained runners. Here we report the influences of body composition and gender on static FAHFA abundances in circulation. We compared the association between circulating FAHFA concentrations and body composition, determined by dual x-ray absorptiometry, in female recreational runners who were lean (BMI < 25 kg/m2, n = 6), to those who were overweight (BMI ≥ 25 kg/m2, n = 7). To characterize the effect of gender we also compared circulating FAHFAs in lean male recreational runners (n = 8) to recreationally trained lean female (n = 6) runner group. Circulating FAHFAs were increased in females in a manner that was modulated by specific adipose depot sizes, blood glucose, and lean body mass. As expected, circulating FAHFAs were diminished in the overweight group, but strikingly, within the lean cohort, increases in circulating FAHFAs were promoted by increased fat mass, relative to lean mass, while the overweight group showed a significantly attenuated relationship. These studies suggest multimodal regulation of circulating FAHFAs and raise hypotheses to test endogenous FAHFA dynamic sources and sinks in health and disease, which will be essential for therapeutic target development. Baseline circulating FAHFA concentrations could signal sub-clinical metabolic dysfunction in metabolically healthy obesity.

Factors contributing to increased workplace violence against nurses during COVID-19 in the healthcare settings of a lower middle-income country- A qualitative study.

PURPOSE: To provide the perceptions of nurses, nursing supervisors, and nursing administrators about factors contributing to increased workplace violence against nurses within the healthcare settings in Pakistan during the first wave of COVID-19 pandemic. METHODS: This study employed a Descriptive Qualitative design, with a purposive sampling technique. From September to December 2021, In-depth Interviews (IDIs) of 45 to 60 minutes, using a semi-structured interview guide, we collected data from a private and a public healthcare setting in Pakistan. Given the travel restrictions during COVID-19, these interviews were conducted online, using Zoom audio features. Bedside nurses, nursing supervisors, and nursing administrators with at least six months of work experience participated in this study. RESULTS: The qualitative data analysis steps suggested by Braun and Clarke (2013) were used for thematic analysis. The overarching theme emerging from the data was "Factors perceived by nurses that contributed to increased workplace violence in their work settings during the first wave of COVID-19, in a lower middle-income country" The sub-themes from the participants' narrations were (a) Highly stressed patients, attendants, and healthcare workers; (b) the financial burden on patients and their families; (c) lack of resources and shortage of staff; (d) restricted visiting policy and a weak security system; (e) lack of awareness about the seriousness of COVID-19; (f) misconceptions about COVID-19 vaccines and nurses' role in disseminating awareness. CONCLUSION: The current pandemic increased the intensity of WPV against nurses in healthcare settings in Pakistan. Despite any supposed reasons for WPV, exposure to violence should never be an acceptable part of nursing. The healthcare system in Pakistan needs to pay equal attention to funding, resource provision, and ensuring a safe working environment for healthcare workers.

A PD-1highCD4+ T Cell Population With a Cytotoxic Phenotype is Associated With Interstitial Lung Disease in Systemic Sclerosis.

OBJECTIVE: T cells contribute to tissue injury in systemic sclerosis (SSc), yet the specific T cell subsets expanded in patients with SSc remain incompletely defined. Here we evaluated specific phenotypes and functions of peripheral helper T (Tph) and follicular helper T (Tfh) cells, which have been implicated in autoantibody production, and assessed their associations with clinical features in a well-characterized cohort of patients with SSc. METHODS: Mass cytometry of T cells from peripheral blood mononuclear cells of patients with SSc and controls were evaluated using t-distributed stochastic neighbor embedding visualization, biaxial gating, and marker expression levels. Findings were validated with flow cytometry and in vitro assays. RESULTS: The frequencies of PD-1highCXCR5+ Tfh cells and PD-1highCXCR5- Tph cells were similar in patients with SSc and controls. t-distributed stochastic neighbor embedding visualization (tSNE) revealed distinct populations within the PD-1highCXCR5- cells distinguished by expression of HLA-DR and inducible costimulator (ICOS). Among PD-1highCXCR5- cells, only the HLA-DR+ICOS- cell population was expanded in patients with SSc. Cytometric and RNA sequencing analyses indicated that these cells expressed cytotoxic rather than B cell helper features. HLA-DR+ICOS- PD-1highCXCR5- cells were less potent in inducing B cell plasmablast differentiation and antibody production than comparator T helper cell populations. HLA-DR+ICOS-PD-1highCXCR5- cells were significantly associated with the presence and severity of interstitial lung disease among patients with SSc. CONCLUSION: Among PD-1highCXCR5- T cells, a subset of HLA-DR+ICOS- cells with cytotoxic features is specifically expanded in patients with SSc and is significantly associated with interstitial lung disease severity. This potential cytotoxicity appearing in the CD4 T cell population can be evaluated as a prognostic disease biomarker in patients with SSc.

Addressing the Black Box of AI - A Model and Research Agenda on the Co-Constitution of Aging and Artificial Intelligence.

Algorithmic technologies and (large) data infrastructures, often referred to as Artificial Intelligence (AI), have received increasing attention from gerontological research in the last decade. While there is much literature that dissects and explores the development, application, and evaluation of AI relevant for gerontology, this article makes a novel contribution by critically engaging with the theorizing in this growing field of research. We observe that gerontology's engagement with AI is shaped by an interventionist logic that situates AI as a black box for gerontological research. We demonstrate how this black box logic has neglected many aspects of AI as a research topic for gerontology and discuss three classical concepts in gerontology to show how they can be used to open various black boxes of aging and AI in the areas: a) the datafication of aging, b) the political economy of AI and aging, and c) everyday engagements and embodiments of AI in later life. In the final chapter, we propose a model of the co-constitution of aging and AI that makes theoretical propositions to study the relational terrain between aging and AI and hence aims to open the black box of AI in gerontology beyond an interventionist logic.