Gait instability and estimated core temperature predict exertional heat stroke.

OBJECTIVE: Exertional heat stroke (EHS), characterised by a high core body temperature (Tcr) and central nervous system (CNS) dysfunction, is a concern for athletes, workers and military personnel who must train and perform in hot environments. The objective of this study was to determine whether algorithms that estimate Tcr from heart rate and gait instability from a trunk-worn sensor system can forward predict EHS onset. METHODS: Heart rate and three-axis accelerometry data were collected from chest-worn sensors from 1806 US military personnel participating in timed 4/5-mile runs, and loaded marches of 7 and 12 miles; in total, 3422 high EHS-risk training datasets were available for analysis. Six soldiers were diagnosed with heat stroke and all had rectal temperatures of >41°C when first measured and were exhibiting CNS dysfunction. Estimated core temperature (ECTemp) was computed from sequential measures of heart rate. Gait instability was computed from three-axis accelerometry using features of pattern dispersion and autocorrelation. RESULTS: The six soldiers who experienced heat stroke were among the hottest compared with the other soldiers in the respective training events with ECTemps ranging from 39.2°C to 40.8°C. Combining ECTemp and gait instability measures successfully identified all six EHS casualties at least 3.5 min in advance of collapse while falsely identifying 6.1% (209 total false positives) examples where exertional heat illness symptoms were neither observed nor reported. No false-negative cases were noted. CONCLUSION: The combination of two algorithms that estimate Tcr and ataxic gate appears promising for real-time alerting of impending EHS.

Myeloid ERK5 deficiency suppresses tumor growth by blocking protumor macrophage polarization via STAT3 inhibition.

Owing to the prevalence of tumor-associated macrophages (TAMs) in cancer and their unique influence upon disease progression and malignancy, macrophage-targeted interventions have attracted notable attention in cancer immunotherapy. However, tractable targets to reduce TAM activities remain very few and far between because the signaling mechanisms underpinning protumor macrophage phenotypes are largely unknown. Here, we have investigated the role of the extracellular-regulated protein kinase 5 (ERK5) as a determinant of macrophage polarity. We report that the growth of carcinoma grafts was halted in myeloid ERK5-deficient mice. Coincidentally, targeting ERK5 in macrophages induced a transcriptional switch in favor of proinflammatory mediators. Further molecular analyses demonstrated that activation of the signal transducer and activator of transcription 3 (STAT3) via Tyr705 phosphorylation was impaired in erk5-deleted TAMs. Our study thus suggests that blocking ERK5 constitutes a treatment strategy to reprogram macrophages toward an antitumor state by inhibiting STAT3-induced gene expression.

Detecting Parkinson's Disease from Wrist-Worn Accelerometry in the U.K. Biobank.

Parkinson's disease (PD) is a chronic movement disorder that produces a variety of characteristic movement abnormalities. The ubiquity of wrist-worn accelerometry suggests a possible sensor modality for early detection of PD symptoms and subsequent tracking of PD symptom severity. As an initial proof of concept for this technological approach, we analyzed the U.K. Biobank data set, consisting of one week of wrist-worn accelerometry from a population with a PD primary diagnosis and an age-matched healthy control population. Measures of movement dispersion were extracted from automatically segmented gait data, and measures of movement dimensionality were extracted from automatically segmented low-movement data. Using machine learning classifiers applied to one week of data, PD was detected with an area under the curve (AUC) of 0.69 on gait data, AUC = 0.84 on low-movement data, and AUC = 0.85 on a fusion of both activities. It was also found that classification accuracy steadily improved across the one-week data collection, suggesting that higher accuracy could be achievable from a longer data collection. These results suggest the viability of using a low-cost and easy-to-use activity sensor for detecting movement abnormalities due to PD and motivate further research on early PD detection and tracking of PD symptom severity.

Tumor-Associated Macrophages in Osteosarcoma: From Mechanisms to Therapy.

Osteosarcomas (OSs) are bone tumors most commonly found in pediatric and adolescent patients characterized by high risk of metastatic progression and recurrence after therapy. Effective therapeutic management of this disease still remains elusive as evidenced by poor patient survival rates. To achieve a more effective therapeutic management regimen, and hence patient survival, there is a need to identify more focused targeted therapies for OSs treatment in the clinical setting. The role of the OS tumor stroma microenvironment plays a significant part in the development and dissemination of this disease. Important components, and hence potential targets for treatment, are the tumor-infiltrating macrophages that are known to orchestrate many aspects of OS stromal signaling and disease progression. In particular, increased infiltration of M2-like tumor-associated macrophages (TAMs) has been associated with OS metastasis and poor patient prognosis despite currently used aggressive therapies regimens. This review aims to provide a summary update of current macrophage-centered knowledge and to discuss the possible roles that macrophages play in the process of OS metastasis development focusing on the potential influence of stromal cross-talk signaling between TAMs, cancer-stem cells and additional OSs tumoral microenvironment factors.

Non-myeloablative busulfan chimeric mouse models are less pro-inflammatory than head-shielded irradiation for studying immune cell interactions in brain tumours.

BACKGROUND: Chimeric mouse models generated via adoptive bone marrow transfer are the foundation for immune cell tracking in neuroinflammation. Chimeras that exhibit low chimerism levels, blood-brain barrier disruption and pro-inflammatory effects prior to the progression of the pathological phenotype, make it difficult to distinguish the role of immune cells in neuroinflammatory conditions. Head-shielded irradiation overcomes many of the issues described and replaces the recipient bone marrow system with donor haematopoietic cells expressing a reporter gene or different pan-leukocyte antigen, whilst leaving the blood-brain barrier intact. However, our previous work with full body irradiation suggests that this may generate a pro-inflammatory peripheral environment which could impact on the brain's immune microenvironment. Our aim was to compare non-myeloablative busulfan conditioning against head-shielded irradiation bone marrow chimeras prior to implantation of glioblastoma, a malignant brain tumour with a pro-inflammatory phenotype. METHODS: Recipient wild-type/CD45.1 mice received non-myeloablative busulfan conditioning (25 mg/kg), full intensity head-shielded irradiation, full intensity busulfan conditioning (125 mg/kg) prior to transplant with whole bone marrow from CD45.2 donors and were compared against untransplanted controls. Half the mice from each group were orthotopically implanted with syngeneic GL-261 glioblastoma cells. We assessed peripheral blood, bone marrow and spleen chimerism, multi-organ pro-inflammatory cytokine profiles at 12 weeks and brain chimerism and immune cell infiltration by whole brain flow cytometry before and after implantation of glioblastoma at 12 and 14 weeks respectively. RESULTS: Both non-myeloablative conditioning and head-shielded irradiation achieve equivalent blood and spleen chimerism of approximately 80%, although bone marrow engraftment is higher in the head-shielded irradiation group and highest in the fully conditioned group. Head-shielded irradiation stimulated pro-inflammatory cytokines in the blood and spleen but not in the brain, suggesting a systemic response to irradiation, whilst non-myeloablative conditioning showed no cytokine elevation. Non-myeloablative conditioning achieved higher donor chimerism in the brain after glioblastoma implantation than head-shielded irradiation with an altered immune cell profile. CONCLUSION: Our data suggest that non-myeloablative conditioning generates a more homeostatic peripheral inflammatory environment than head-shielded irradiation to allow a more consistent evaluation of immune cells in glioblastoma and can be used to investigate the roles of peripheral immune cells and bone marrow-derived subsets in other neurological diseases.

Novel carbonic anhydrase IX-targeted therapy enhances the anti-tumour effects of cisplatin in small cell lung cancer.

Small cell lung cancer (SCLC) has an extremely poor prognosis and methods of improving chemotherapeutic intervention are much sought after. A promising approach lies in inhibiting the tumour-associated enzyme, carbonic anhydrase IX (CA IX), which supports tumour cell survival. The aim of this study was to assess the potential of CA IX inhibition using 4-(3'-(3″,5″-dimethylphenyl)ureido)phenyl sulfamate (S4), for the treatment of human SCLC alone and in combination with cisplatin chemotherapy. Treating SCLC cell lines (DMS 79 and COR-L24) with 100 µM S4 reduced viability in vitro and enhanced cell death when combined with 7 µM cisplatin, most prominently under hypoxic conditions (0.1% O2 ). When either cell line was grown as a xenograft tumour in nude mice, intraperitoneal injection of 50 mg/kg S4 alone and in combination with 3 mg/kg cisplatin led to significantly reduced tumour growth. Combination therapy was superior to single agents and response was greatly accentuated when administering repeated doses of cisplatin in DMS 79 tumours. The mechanism of therapeutic response was investigated in vitro, where S4 treatment increased apoptosis under hypoxic conditions in both DMS 79 and COR-L24 cells. DMS 79 tumours receiving S4 in vivo also displayed increased apoptosis and necrosis. Combining S4 with cisplatin reduced both the area of hypoxia and CA IX-positive cells within tumours and increased necrosis, suggesting hypoxia-specific targeting. This study presents a novel, targeted approach to improving current SCLC therapy via inhibition of CA IX, which enhances apoptosis and significantly inhibits xenograft tumour growth when administered alone and in combination with cisplatin chemotherapy.

A novel systemically administered Toll-like receptor 7 agonist potentiates the effect of ionizing radiation in murine solid tumor models.

Although topical TLR7 therapies such as imiquimod have proved successful in the treatment of dermatological malignancy, systemic delivery may be required for optimal immunotherapy of nondermatological tumors. We report that intravenous delivery of the novel small molecule TLR7 agonist, DSR-6434, leads to the induction of type 1 interferon and activation of T and B lymphocytes, NK and NKT cells. Our data demonstrate that systemic administration of DSR-6434 enhances the efficacy of ionizing radiation (IR) and leads to improved survival in mice bearing either CT26 or KHT tumors. Of the CT26 tumor-bearing mice that received combined therapy, 55% experienced complete tumor resolution. Our data reveal that these long-term surviving mice have a significantly greater frequency of tumor antigen specific CD8(+) T cells when compared to age-matched tumor-naïve cells. To evaluate therapeutic effects on spontaneous metastases, we showed that combination of DSR-6434 with local IR of the primary tumor significantly reduced metastatic burden in the lung, when compared to time-matched cohorts treated with IR alone. The data demonstrate that systemic administration of the novel TLR7 agonist DSR-6434 in combination with IR primes an antitumor CD8(+) T-cell response leading to improved survival in syngeneic models of colorectal carcinoma and fibrosarcoma. Importantly, efficacy extends to sites outside of the field of irradiation, reducing metastatic load. Clinical evaluation of systemic TLR7 therapy in combination with IR for the treatment of solid malignancy is warranted.

Implementation of Oxygen Enhanced Magnetic Resonance Imaging (OE-MRI) and a Pilot Genomic Study of Hypoxia in Bladder Cancer Xenografts.

BACKGROUND/AIM: Patients with hypoxic bladder cancer benefit from hypoxia modification added to radiotherapy, but no biomarkers exist to identify patients with hypoxic tumours. We, herein, aimed to implement oxygen-enhanced MRI (OE-MRI) in xenografts derived from muscle-invasive bladder cancer (MIBC) for future hypoxia biomarker discovery work; and generate gene expression data for future biomarker discovery. MATERIALS AND METHODS: The flanks of female CD-1 nude mice inoculated with HT1376 MIBC cells. Mice with small (300 mm3) or large (700 mm3) tumours were imaged, breathing air then 100% O2, 1 h post injection with pimonidazole in an Agilant 7T 16cm bore magnet interfaced to a Bruker Avance III console with a T2-TurboRARE sequence using a dynamic MPRAGE acquisition. Dynamic Spoiled Gradient Recalled Echo images were acquired for 5 min, with 0.1mmol/kg Gd-DOTA (Dotarem, Guerbet, UK) injected after 60 s (1 ml/min). Voxel size and field of view of dynamic contrast enhanced (DCE)-MRI and OE-MRI scans were matched. The voxels considered as perfused with significant post-contrast enhancement (p<0.05) in DCE-MRI scans and tissue were further split into pOxyE (normoxic) and pOxyR (hypoxic) regions. Tumours harvested in liquid N2, sectioned, RNA was extracted and transcriptomes analysed using Clariom S microarrays. RESULTS: Imaged hypoxic regions were greater in the larger versus smaller tumour. Expression of known hypoxia-inducible genes and a 24 gene bladder cancer hypoxia score were higher in pimonidazole-high versus -low regions: CA9 (p=0.012) and SLC2A1 (p=0.012) demonstrating expected transcriptomic behaviour. CONCLUSION: OE-MRI was successfully implemented in MIBC-derived xenografts. Transcriptomic data derived from hypoxic and non-hypoxic xenograft regions will be useful for future studies.

Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.

The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.

Targeting Hypoxia-Inducible Factor-1α in Pancreatic Cancer: siRNA Delivery Using Hyaluronic Acid-Displaying Nanoparticles.

Background/Objectives: Conventional anticancer therapies often lack specificity, targeting both cancerous and normal cells, which reduces efficacy and leads to undesired off-target effects. An additional challenge is the presence of hypoxic regions in tumors, where the Hypoxia Inducible Factor (HIF) transcriptional system drives the expression of pro-survival and drug resistance genes, leading to radio- and chemo-resistance. This study aims to explore the efficacy of targeted nanoparticle (NP)-based small interfering RNA (siRNA) therapies in downregulating these genes to enhance treatment outcomes in pancreatic cancer, a tumor type characterized by high CD44 expression and hypoxia. Methods: We utilized hyaluronic acid (HA)-displaying nanoparticles composed of positively charged chitosan (CS) complexed with siRNA to target and knock down HIF-1α in pancreatic cancer cells. Two NP formulations were prepared using either low molecular weight (LMW) or high molecular weight (HMW) CS. These formulations were evaluated for their internalization by cells and their effectiveness in gene silencing, both in vitro and in vivo. Results: The study found that the molecular weight (MW) of CS influenced the interaction between HA and CD44, as well as the release of siRNA upon internalization. The LMW CS formulation shows faster uptake kinetics, while HMW CS is more effective in gene knockdown across different cell lines in vitro. In vivo, both were able to significantly knockdown HIF-1α and some of its downstream genes. Conclusions: The results suggest that HMW and LMW CS-based NPs exhibit distinct characteristics, showing that both MWs have potential for targeted pancreatic cancer therapy by influencing different aspects of delivery and gene silencing, particularly in the hypoxic tumor microenvironment.

Endothelial heparan sulfate 6-O-sulfation levels regulate angiogenic responses of endothelial cells to fibroblast growth factor 2 and vascular endothelial growth factor.

Fibroblast growth factor 2 (FGF2) and vascular endothelial growth factor 165 (VEGF(165)) are potent pro-angiogenic growth factors that play a pivotal role in tumor angiogenesis. The activity of these growth factors is regulated by heparan sulfate (HS), which is essential for the formation of FGF2/FGF receptor (FGFR) and VEGF(165)/VEGF receptor signaling complexes. However, the structural characteristics of HS that determine activation or inhibition of such complexes are only partially defined. Here we show that ovarian tumor endothelium displays high levels of HS sequences that harbor glucosamine 6-O-sulfates when compared with normal ovarian vasculature where these sequences are also detected in perivascular area. Reduced HS 6-O-sulfotransferase 1 (HS6ST-1) or 6-O-sulfotransferase 2 (HS6ST-2) expression in endothelial cells impacts upon the prevalence of HS 6-O-sulfate moieties in HS sequences, which consist of repeating short, highly sulfated S domains interspersed by transitional N-acetylated/N-sulfated domains. 1-40% reduction in 6-O-sulfates significantly compromises FGF2- and VEGF(165)-induced endothelial cell sprouting and tube formation in vitro and FGF2-dependent angiogenesis in vivo. Moreover, HS on wild-type neighboring endothelial or smooth muscle cells fails to restore endothelial cell sprouting and tube formation. The affinity of FGF2 for HS with reduced 6-O-sulfation is preserved, although FGFR1 activation is inhibited correlating with reduced receptor internalization. These data show that 6-O-sulfate moieties in endothelial HS are of major importance in regulating FGF2- and VEGF(165)-dependent endothelial cell functions in vitro and in vivo and highlight HS6ST-1 and HS6ST-2 as potential targets of novel antiangiogenic agents.

Automated B-line scoring on thoracic sonography.

With the proliferation of portable sonography and the increase in nontraditional users, there is an increased need for automated decision support to standardize results. We developed algorithms to evaluate the presence or absence of "B-lines" on thoracic sonography as a marker for interstitial fluid. Algorithm performance was compared against an average of scores from 2 expert clinical sonographers. On the set for algorithm development, 90% of the scores matched the average expert scores with differences of 1 or less. On the independent set, a perfect match was achieved. We believe that these are the first reported results in computerized B-line scoring.

Individualized Ultrasound-Guided Intervention Phantom Development, Fabrication, and Proof of Concept.

Commercial ultrasound vascular phantoms lack the anatomic diversity required for robust pre-clinical interventional device testing. We fabricated individualized phantoms to test an artificial intelligence enabled ultrasound-guided surgical robotic system (AI-GUIDE) which allows novices to cannulate deep vessels. After segmenting vessels on computed tomography scans, vessel cores, bony anatomy, and a mold tailored to the skin contour were 3D-printed. Vessel cores were coated in silicone, surrounded in tissue-mimicking gel tailored for ultrasound and needle insertion, and dissolved with water. One upper arm and four inguinal phantoms were constructed. Operators used AI-GUIDE to deploy needles into phantom vessels. Two groin phantoms were tested due to imaging artifacts in the other two phantoms. Six operators (medical experience: none, 3; 1-5 years, 2; 5+ years, 1) inserted 27 inguinal needles with 81% (22/27) success in a median of 48 seconds. Seven operators performed 24 arm injections, without tuning the AI for arm anatomy, with 71% (17/24) success. After excluding failures due to motor malfunction and a defective needle, success rate was 100% (22/22) in the groin and 85% (17/20) in the arm. Individualized 3D-printed phantoms permit testing of surgical robotics across a large number of operators and different anatomic sites. AI-GUIDE operators rapidly and reliably inserted a needle into target vessels in the upper arm and groin, even without prior medical training. Virtual device trials in individualized 3-D printed phantoms may improve rigor of results and expedite translation.Clinical Relevance- Individualized phantoms enable rigorous and efficient evaluation of interventional devices and reduce the need for animal and human subject testing.

PARP14 inhibition restores PD-1 immune checkpoint inhibitor response following IFNγ-driven acquired resistance in preclinical cancer models.

Resistance mechanisms to immune checkpoint blockade therapy (ICBT) limit its response duration and magnitude. Paradoxically, Interferon γ (IFNγ), a key cytokine for cellular immunity, can promote ICBT resistance. Using syngeneic mouse tumour models, we confirm that chronic IFNγ exposure confers resistance to immunotherapy targeting PD-1 (α-PD-1) in immunocompetent female mice. We observe upregulation of poly-ADP ribosyl polymerase 14 (PARP14) in chronic IFNγ-treated cancer cell models, in patient melanoma with elevated IFNG expression, and in melanoma cell cultures from ICBT-progressing lesions characterised by elevated IFNγ signalling. Effector T cell infiltration is enhanced in tumours derived from cells pre-treated with IFNγ in immunocompetent female mice when PARP14 is pharmacologically inhibited or knocked down, while the presence of regulatory T cells is decreased, leading to restoration of α-PD-1 sensitivity. Finally, we determine that tumours which spontaneously relapse in immunocompetent female mice following α-PD-1 therapy upregulate IFNγ signalling and can also be re-sensitised upon receiving PARP14 inhibitor treatment, establishing PARP14 as an actionable target to reverse IFNγ-driven ICBT resistance.

Feature Importance Analysis for Compensatory Reserve to Predict Hemorrhagic Shock.

Hemorrhage is the leading cause of preventable death from trauma. Traditionally, vital signs have been used to detect blood loss and possible hemorrhagic shock. However, vital signs are not sensitive for early detection because of physiological mechanisms that compensate for blood loss. As an alternative, machine learning algorithms that operate on an arterial blood pressure (ABP) waveform acquired via photoplethysmography have been shown to provide an effective early indicator. However, these machine learning approaches lack physiological interpretability. In this paper, we evaluate the importance of nine ABP-derived features that provide physiological insight, using a database of 40 human subjects from a lower-body negative pressure model of progressive central hypovolemia. One feature was found to be considerably more important than any other. That feature, the half-rise to dicrotic notch (HRDN), measures an approximate time delay between the ABP ejected and reflected wave components. This delay is an indication of compensatory mechanisms such as reduced arterial compliance and vasoconstriction. For a scale of 0% to 100%, with 100% representing normovolemia and 0% representing decompensation, linear regression of the HRDN feature results in root-mean-squared error of 16.9%, R2 of 0.72, and an area under the receiver operating curve for detecting decompensation of 0.88. These results are comparable to previously reported results from the more complex black box machine learning models. Clinical Relevance- A single physiologically interpretable feature measured from an arterial blood pressure waveform is shown to be effective in monitoring for blood loss and impending hemorrhagic shock based on data from a human lower-body negative pressure model of progressive central hypolemia.

Noninvasive Monitoring of Simulated Hemorrhage and Whole Blood Resuscitation.

Hemorrhage is the leading cause of preventable death from trauma. Accurate monitoring of hemorrhage and resuscitation can significantly reduce mortality and morbidity but remains a challenge due to the low sensitivity of traditional vital signs in detecting blood loss and possible hemorrhagic shock. Vital signs are not reliable early indicators because of physiological mechanisms that compensate for blood loss and thus do not provide an accurate assessment of volume status. As an alternative, machine learning (ML) algorithms that operate on an arterial blood pressure (ABP) waveform have been shown to provide an effective early indicator. However, these ML approaches lack physiological interpretability. In this paper, we evaluate and compare the performance of ML models trained on nine ABP-derived features that provide physiological insight, using a database of 13 human subjects from a lower-body negative pressure (LBNP) model of progressive central hypovolemia and subsequent progressive restoration to normovolemia (i.e., simulated hemorrhage and whole blood resuscitation). Data were acquired at multiple repressurization rates for each subject to simulate varying resuscitation rates, resulting in 52 total LBNP collections. This work is the first to use a single ABP-based algorithm to monitor both simulated hemorrhage and resuscitation. A gradient-boosted regression tree model trained on only the half-rise to dicrotic notch (HRDN) feature achieved a root-mean-square error (RMSE) of 13%, an R2 of 0.82, and area under the receiver operating characteristic curve of 0.97 for detecting decompensation. This single-feature model's performance compares favorably to previously reported results from more-complex black box machine learning models. This model further provides physiological insight because HRDN represents an approximate measure of the delay between the ABP ejected and reflected wave and therefore is an indication of cardiac and peripheral vascular mechanisms that contribute to the compensatory response to blood loss and replacement.

Transfer Learning for Automated COVID-19 B-Line Classification in Lung Ultrasound.

Lung ultrasound (LUS) as a diagnostic tool is gaining support for its role in the diagnosis and management of COVID-19 and a number of other lung pathologies. B-lines are a predominant feature in COVID-19, however LUS requires a skilled clinician to interpret findings. To facilitate the interpretation, our main objective was to develop automated methods to classify B-lines as pathologic vs. normal. We developed transfer learning models based on ResNet networks to classify B-lines as pathologic (at least 3 B-lines per lung field) vs. normal using COVID-19 LUS data. Assessment of B-line severity on a 0-4 multi-class scale was also explored. For binary B-line classification, at the frame-level, all ResNet models pretrained with ImageNet yielded higher performance than the baseline nonpretrained ResNet-18. Pretrained ResNet-18 has the best Equal Error Rate (EER) of 9.1% vs the baseline of 11.9%. At the clip-level, all pretrained network models resulted in better Cohen's kappa agreement (linear-weighted) and clip score accuracy, with the pretrained ResNet-18 having the best Cohen's kappa of 0.815 [95% CI: 0.804-0.826], and ResNet-101 the best clip scoring accuracy of 93.6%. Similar results were shown for multi-class scoring, where pretrained network models outperformed the baseline model. A class activation map is also presented to guide clinicians in interpreting LUS findings. Future work aims to further improve the multi-class assessment for severity of B-lines with a more diverse LUS dataset.

Inhibiting ERK5 overcomes breast cancer resistance to anti-HER2 therapy by targeting the G1/S cell cycle transition.

Targeting the human epidermal growth factor receptor 2 (HER2) became a landmark in the treatment of HER2-driven breast cancer. Nonetheless, the clinical efficacy of anti-HER2 therapies can be short-lived and a significant proportion of patients ultimately develop metastatic disease and die. One striking consequence of oncogenic activation of HER2 in breast cancer cells is the constitutive activation of the extracellular-regulated protein kinase 5 (ERK5) through its hyperphosphorylation. In this study, we sought to decipher the significance of this unique molecular signature in promoting therapeutic resistance to anti-HER2 agents. We found that a small-molecule inhibitor of ERK5 suppressed the phosphorylation of the retinoblastoma protein (RB) in HER2 positive breast cancer cells. As a result, ERK5 inhibition enhanced the anti-proliferative activity of single-agent anti-HER2 therapy in resistant breast cancer cell lines by causing a G1 cell cycle arrest. Moreover, ERK5 knockdown restored the anti-tumor activity of the anti-HER2 agent lapatinib in human breast cancer xenografts. Taken together, these findings support the therapeutic potential of ERK5 inhibitors to improve the clinical benefit that patients receive from targeted HER2 therapies.

Oxidative stress from DGAT1 oncoprotein inhibition in melanoma suppresses tumor growth when ROS defenses are also breached.

Dysregulated cellular metabolism is a cancer hallmark for which few druggable oncoprotein targets have been identified. Increased fatty acid (FA) acquisition allows cancer cells to meet their heightened membrane biogenesis, bioenergy, and signaling needs. Excess FAs are toxic to non-transformed cells but surprisingly not to cancer cells. Molecules underlying this cancer adaptation may provide alternative drug targets. Here, we demonstrate that diacylglycerol O-acyltransferase 1 (DGAT1), an enzyme integral to triacylglyceride synthesis and lipid droplet formation, is frequently up-regulated in melanoma, allowing melanoma cells to tolerate excess FA. DGAT1 over-expression alone transforms p53-mutant zebrafish melanocytes and co-operates with oncogenic BRAF or NRAS for more rapid melanoma formation. Antagonism of DGAT1 induces oxidative stress in melanoma cells, which adapt by up-regulating cellular reactive oxygen species defenses. We show that inhibiting both DGAT1 and superoxide dismutase 1 profoundly suppress tumor growth through eliciting intolerable oxidative stress.

Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study.

Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830]). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables.