Crystal structures of NAD(P)H nitroreductases from Klebsiella pneumoniae.

Klebsiella pneumoniae (Kp) is an infectious disease pathogen that poses a significant global health threat due to its potential to cause severe infections and its tendency to exhibit multidrug resistance. Understanding the enzymatic mechanisms of the oxygen-insensitive nitroreductases (Kp-NRs) from Kp is crucial for the development of effective nitrofuran drugs, such as nitrofurantoin, that can be activated as antibiotics. In this paper, three crystal structures of two Kp-NRs (PDB entries 7tmf/7tmg and 8dor) are presented, and an analysis of their crystal structures and their flavin mononucleotide (FMN)-binding mode is provided. The structures with PDB codes 7tmf (Kp-NR1a), 7tmg (Kp-NR1b) and 8dor (Kp-NR2) were determined at resolutions of 1.97, 1.90 and 1.35 Å, respectively. The Kp-NR1a and Kp-NR1b structures adopt an αß fold, in which four-stranded antiparallel ß-sheets are surrounded by five helices. With domain swapping, the ß-sheet was expanded with a ß-strand from the other molecule of the dimer. The difference between the structures lies in the loop spanning Leu173-Ala185: in Kp-NR1a the loop is disordered, whereas the loop adopts multiple conformations in Kp-NR1b. The FMN interactions within Kp-NR1/NR2 involve hydrogen-bond and π-stacking interactions. Kp-NR2 contains four-stranded antiparallel ß-sheets surrounded by eight helices with two short helices and one ß-sheet. Structural and sequence alignments show that Kp-NR1a/b and Kp-NR2 are homologs of the Escherichia coli oxygen-insensitive NRs YdjA and NfnB and of Enterobacter cloacae NR, respectively. By homology inference from E. coli, Kp-NR1a/b and Kp-NR2 may detoxify polynitroaromatic compounds and Kp-NR2 may activate nitrofuran drugs to cause bactericidal activity through a ping-pong bi-bi mechanism, respectively.

Capture rates of Eptesicus fuscus increase following white-nose syndrome across the eastern US.

Emerging infectious diseases threaten wildlife globally. While the effects of infectious diseases on hosts with severe infections and high mortality rates often receive considerable attention, effects on hosts that persist despite infection are less frequently studied. To understand how persisting host populations change in the face of disease, we quantified changes to the capture rates of Eptesicus fuscus (big brown bats), a persisting species susceptible to infection by the invasive fungal pathogen Pseudogymnoascus destructans (Pd; causative agent for white-nose syndrome), across the eastern US using a 30-year dataset. Capture rates of male and female E. fuscus increased from preinvasion to pathogen establishment years, with greater increases to the capture rates of females than males. Among females, capture rates of pregnant and post-lactating females increased by pathogen establishment. We outline potential mechanisms for these broad demographic changes in E. fuscus capture rates (i.e., increases to foraging from energy deficits created by Pd infection, increases to relative abundance, or changes to reproductive cycles), and suggest future research for identifying mechanisms for increasing capture rates across the eastern US. These data highlight the importance of understanding how populations of persisting host species change following pathogen invasion across a broad spatial scale. Understanding changes to population composition following pathogen invasion can identify broad ecological patterns across space and time, and open new avenues for research to identify drivers of those patterns.

Skin cancer screening recommendations by U.S. cancer centers: Inconsistency with national guidelines.

OBJECTIVE: The incidence of melanoma has increased dramatically over the past four decades, while overall mortality has remained stable. This increase in incidence without a change in overall mortality may be due to overdiagnosis through skin cancer screening. Despite the USPSTF citing insufficient evidence for or against professional skin cancer screening in average-risk adults, U.S. skin cancer screening practices may be leading to overdiagnosis of skin cancers. METHODS: Two reviewers examined the online recommendations for skin cancer screening of 1113 U.S. cancer centers accredited by the Commission on Cancer, including 66 designated by the National Cancer Institute (NCI). Recommendations on skin cancer screening, such as age, frequency, and patient population (i.e. high-risk of developing skin cancer, "people of color") were documented. RESULTS: We found that 18% of centers (202) recommended professional screening in average-risk adults, 35.8% (399) advised regular self-examination, and only 3.4% (38) cited insufficient evidence for screening practices; 49% of NCI centers (32/66) recommended screening in high-risk adults compared to 13% of non-NCI centers (135/1047; p = 0.0004); 0.45% of centers (5) mentioned the potential harms of screening, while 3.5% (39) specifically recommended screening for people of color. CONCLUSION: Our study reveals that many U.S. cancer centers advise some form of skin cancer screening despite a lack of evidence for or against these practices. Few centers mentioned the potential harms of screening, including overdiagnosis. This indicates a need for stronger evidence for specific screening guidelines and for greater public awareness of the potential benefits and harms of routine skin cancer screening.

Exploiting ChatGPT for Diagnosing Autism-Associated Language Disorders and Identifying Distinct Features.

Diagnosing language disorders associated with autism is a complex and nuanced challenge, often hindered by the subjective nature and variability of traditional assessment methods. Traditional diagnostic methods not only require intensive human effort but also often result in delayed interventions due to their lack of speed and specificity. In this study, we explored the application of ChatGPT, a state-of-the-art large language model, to overcome these obstacles by enhancing diagnostic accuracy and profiling specific linguistic features indicative of autism. Leveraging ChatGPT's advanced natural language processing capabilities, this research aims to streamline and refine the diagnostic process. Specifically, we compared ChatGPT's performance with that of conventional supervised learning models, including BERT, a model acclaimed for its effectiveness in various natural language processing tasks. We showed that ChatGPT substantially outperformed these models, achieving over 13% improvement in both accuracy and F1-score in a zero-shot learning configuration. This marked enhancement highlights the model's potential as a superior tool for neurological diagnostics. Additionally, we identified ten distinct features of autism-associated language disorders that vary significantly across different experimental scenarios. These features, which included echolalia, pronoun reversal, and atypical language usage, were crucial for accurately diagnosing ASD and customizing treatment plans. Together, our findings advocate for adopting sophisticated AI tools like ChatGPT in clinical settings to assess and diagnose developmental disorders. Our approach not only promises greater diagnostic precision but also aligns with the goals of personalized medicine, potentially transforming the evaluation landscape for autism and similar neurological conditions.

Splitting light pollution: Wavelength effects on the activity of two sandy beach species.

Artificial Light at Night (ALAN) threatens to disrupt most natural habitats and species, including those in coastal settings, where a growing number of studies have identified ALAN impacts. A careful examination of the light properties behind those impacts is important to better understand and manage the effects of this stressor. This study focused on ALAN monochromatic wavelengths and examined which types of light spectra altered the natural activity of two prominent coastal species from the Pacific southeast: the talitroid amphipod Orchestoidea tuberculata and the oniscoid isopod Tylos spinulosus. We compared the natural daylight/night activity of these organisms with the one they exhibit when exposed to five different ALAN wavelengths: lights in the violet, blue, green, amber, and red spectra. Our working hypothesis was that ALAN alters these species' activity at night, but the magnitude of such impact differs depending on light wavelengths. Measurements of activity over 24 h cycles for five consecutive days and in three separate experiments confirmed a natural circadian activity pattern in both species, with strong activity at night (∼90% of probability) and barely any activity during daylight. However, when exposed to ALAN, activity declined significantly in both species under all light wavelengths. Interestingly, amphipods exhibited moderate activity (∼40% of probability) when exposed to red lights at night, whereas isopods shifted some of their activity to daylight hours in two of the experiments when exposed to blue or amber lights, suggesting a possible alteration in this species circadian rhythm. Altogether, our results were consistent with our working hypothesis, and suggest that ALAN reduces night activity, and some wavelengths have differential effects on each species. Differences between amphipods and isopods are likely related to their distinct adaptations to natural low-light habitat conditions, and therefore distinct sensitivity to ALAN.

Imaginative elaboration in agenesis of the corpus callosum: topic modeling and perplexity.

OBJECTIVE: Previous studies have found deficits in imaginative elaboration and social inference to be associated with agenesis of the corpus callosum (ACC; Renteria-Vasquez et al., 2022; Turk et al., 2009). In the current study, Thematic Apperception Test (TAT) responses from a neurotypical control group and a group of individuals with ACC were used to further study the capacity for imaginative elaboration and story coherence. METHOD: Topic modeling was employed utilizing Latent Diritchlet Allocation to characterize the narrative responses to the pictures used in the TAT. A measure of the difference between models (perplexity) was used to compare the topics of the responses of individual participants to the common core model derived from the responses of the control group. Story coherence was tested using sentence-to-sentence Latent Semantic Analysis. RESULTS: Group differences in perplexity were statistically significant overall, and for each card individually (p < .001). There were no differences between the groups in story coherence. CONCLUSIONS: TAT narratives from persons with ACC were normally coherent, but more conventional (i.e., more similar to the core text) compared to those of neurotypical controls. Individuals with ACC can make conventional social inferences about socially ambiguous stimuli, but are restricted in their imaginative elaborations, resulting in less topical variability (lower perplexity values) compared to neurotypical controls.

Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People With Type 2 Diabetes.

OBJECTIVE: To identify genetic risk factors for incident cardiovascular disease (CVD) among people with type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: We conducted a multiancestry time-to-event genome-wide association study for incident CVD among people with T2D. We also tested 204 known coronary artery disease (CAD) variants for association with incident CVD. RESULTS: Among 49,230 participants with T2D, 8,956 had incident CVD events (event rate 18.2%). We identified three novel genetic loci for incident CVD: rs147138607 (near CACNA1E/ZNF648, hazard ratio [HR] 1.23, P = 3.6 × 10-9), rs77142250 (near HS3ST1, HR 1.89, P = 9.9 × 10-9), and rs335407 (near TFB1M/NOX3, HR 1.25, P = 1.5 × 10-8). Among 204 known CAD loci, 5 were associated with incident CVD in T2D (multiple comparison-adjusted P < 0.00024, 0.05/204). A standardized polygenic score of these 204 variants was associated with incident CVD with HR 1.14 (P = 1.0 × 10-16). CONCLUSIONS: The data point to novel and known genomic regions associated with incident CVD among individuals with T2D.

Smartphone-based gaze estimation for in-home autism research.

Atypical gaze patterns are a promising biomarker of autism spectrum disorder. To measure gaze accurately, however, it typically requires highly controlled studies in the laboratory using specialized equipment that is often expensive, thereby limiting the scalability of these approaches. Here we test whether a recently developed smartphone-based gaze estimation method could overcome such limitations and take advantage of the ubiquity of smartphones. As a proof-of-principle, we measured gaze while a small sample of well-assessed autistic participants and controls watched videos on a smartphone, both in the laboratory (with lab personnel) and in remote home settings (alone). We demonstrate that gaze data can be efficiently collected, in-home and longitudinally by participants themselves, with sufficiently high accuracy (gaze estimation error below 1° visual angle on average) for quantitative, feature-based analysis. Using this approach, we show that autistic individuals have reduced gaze time on human faces and longer gaze time on non-social features in the background, thereby reproducing established findings in autism using just smartphones and no additional hardware. Our approach provides a foundation for scaling future research with larger and more representative participant groups at vastly reduced cost, also enabling better inclusion of underserved communities.

Artificial light at night alters the feeding activity and two molecular indicators in the plumose sea anemone Metridium senile (L.).

Artificial light at night (ALAN) is becoming a widespread stressor in coastal ecosystems, affecting species that rely on natural day/night cycles. Yet, studies examining ALAN effects remain limited, particularly in the case of sessile species. This study assessed the effects of ALAN upon the feeding activity and two molecular indicators in the widespread plumose sea anemone Metridium senile. Anemones were exposed to either natural day/night or ALAN conditions to monitor feeding activity, and tissue samples were collected to quantify proteins and superoxide dismutase (SOD) enzyme concentrations. In day/night conditions, sea anemones showed a circadian rhythm of activity in which feeding occurs primarily at night. This rhythm was altered by ALAN, which turned it into a reduced and more uniform pattern of feeding. Consistently, proteins and SOD concentrations were significantly lower in anemones exposed to ALAN, suggesting that ALAN can be harmful to sea anemones and potentially other marine sessile species.

EMP2 Serves as a Functional Biomarker for Chemotherapy-Resistant Triple-Negative Breast Cancer.

Breast cancer (BC) remains among the most commonly diagnosed cancers in women worldwide. Triple-negative BC (TNBC) is a subset of BC characterized by aggressive behavior, a high risk of distant recurrence, and poor overall survival rates. Chemotherapy is the backbone for treatment in patients with TNBC, but outcomes remain poor compared to other BC subtypes, in part due to the lack of recognized functional targets. In this study, the expression of the tetraspan protein epithelial membrane protein 2 (EMP2) was explored as a predictor of TNBC response to standard chemotherapy. We demonstrate that EMP2 functions as a prognostic biomarker for patients treated with taxane-based chemotherapy, with high expression at both transcriptomic and protein levels following treatment correlating with poor overall survival. Moreover, we show that targeting EMP2 in combination with docetaxel reduces tumor load in syngeneic and xenograft models of TNBC. These results provide support for the prognostic and therapeutic potential of this tetraspan protein, suggesting that anti-EMP2 therapy may be beneficial for the treatment of select chemotherapy-resistant TNBC tumors.

Outcomes of atypical lobular hyperplasia managed by active surveillance or immediate surgery.

BACKGROUND: Atypical lobular hyperplasia (ALH) is typically diagnosed via needle core biopsy (NCB) and is commonly removed surgically in light of upgrade to malignancy rates of 1%-5%. As studies on radiographic outcomes of ALH managed by active surveillance (AS) are limited, we investigated the upgrade rates of surgically excised ALH as well as radiographic progression during AS. METHODS: In this retrospective study, 125 patients with 127 ALH lesions diagnosed via NCB at Weill Cornell Medicine from 2015 to 2021 were included. The upgrade rate to cancer was determined for patients who had surgical management ≤6 months after biopsy. Among patients with ALH managed by AS, we investigated radiographic progression on 6-month interval imaging. RESULTS: Of 127 ALH lesions, 75% (n = 95) were immediately excised and 25% (n = 32) were observed under AS. The upgrade rate of immediately excised ALH was 2.1% (n = 2; invasive ductal carcinoma [IDC], T1N0 and IDC, and T1Nx). In the AS cohort, no ALH lesions progressed radiographically during the follow-up period of 22.5 months (median), with all remaining stable (50%, n = 16), resolving (47%, n = 15), or decreasing in size (3%, n = 1). CONCLUSIONS: In this study, NCB-diagnosed ALH had a low upgrade to malignancy rate (2.1%), and no ALH lesions managed by AS progressed radiographically during the follow-up period of 22.5 months. These results support AS as the favorable option for patients with pure ALH on biopsy, with surgical excision for lesions that progress on surveillance.

AI Applications to Breast MRI: Today and Tomorrow.

In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.

A Compound That Inhibits Glycolysis in Prostate Cancer Controls Growth of Advanced Prostate Cancer.

Metastatic castration-resistant prostate cancer remains incurable regardless of recent therapeutic advances. Prostate cancer tumors display highly glycolytic phenotypes as the cancer progresses. Nonspecific inhibitors of glycolysis have not been utilized successfully for chemotherapy, because of their penchant to cause systemic toxicity. This study reports the preclinical activity, safety, and pharmacokinetics of a novel small-molecule preclinical candidate, BKIDC-1553, with antiglycolytic activity. We tested a large battery of prostate cancer cell lines for inhibition of cell proliferation, in vitro. Cell-cycle, metabolic, and enzymatic assays were used to demonstrate their mechanism of action. A human patient-derived xenograft model implanted in mice and a human organoid were studied for sensitivity to our BKIDC preclinical candidate. A battery of pharmacokinetic experiments, absorption, distribution, metabolism, and excretion experiments, and in vitro and in vivo toxicology experiments were carried out to assess readiness for clinical trials. We demonstrate a new class of small-molecule inhibitors where antiglycolytic activity in prostate cancer cell lines is mediated through inhibition of hexokinase 2. These compounds display selective growth inhibition across multiple prostate cancer models. We describe a lead BKIDC-1553 that demonstrates promising activity in a preclinical xenograft model of advanced prostate cancer, equivalent to that of enzalutamide. BKIDC-1553 demonstrates safety and pharmacologic properties consistent with a compound that can be taken into human studies with expectations of a good safety margin and predicted dosing for efficacy. This work supports testing BKIDC-1553 and its derivatives in clinical trials for patients with advanced prostate cancer.

89Zr-ImmunoPET for the Specific Detection of EMP2-Positive Tumors.

Epithelial membrane protein-2 (EMP2) is upregulated in a number of tumors and therefore remains a promising target for mAb-based therapy. In the current study, image-guided therapy for an anti-EMP2 mAb was evaluated by PET in both syngeneic and immunodeficient cancer models expressing different levels of EMP2 to enable a better understanding of its tumor uptake and off target accumulation and clearance. The therapeutic efficacy of the anti-EMP2 mAb was initially evaluated in high- and low-expressing tumors, and the mAb reduced tumor load for the high EMP2-expressing 4T1 and HEC-1-A tumors. To create an imaging agent, the anti-EMP2 mAb was conjugated to p-SCN-Bn-deferoxamine (DFO) and radiolabeled with 89Zr. Tumor targeting and tissue biodistribution were evaluated in syngeneic tumor models (4T1, CT26, and Panc02) and human tumor xenograft models (Ramos, HEC-1-A, and U87MG/EMP2). PET imaging revealed radioactive accumulation in EMP2-positive tumors within 24 hours after injection, and the signal was retained for 5 days. High specific uptake was observed in tumors with high EMP2 expression (4T1, CT26, HEC-1-A, and U87MG/EMP2), with less accumulation in tumors with low EMP2 expression (Panc02 and Ramos). Biodistribution at 5 days after injection revealed that the tumor uptake ranged from 2 to approximately 16%ID/cc. The results show that anti-EMP2 mAbs exhibit EMP2-dependent tumor uptake with low off-target accumulation in preclinical cancer models. The development of improved anti-EMP2 Ab fragments may be useful to track EMP2-positive tumors for subsequent therapeutic interventions.

Clofazimine pharmacokinetics in HIV-infected adults with diarrhea: Implications of diarrheal disease on absorption of orally administered therapeutics.

Oral drug absorption kinetics are usually established in populations with a properly functioning gastrointestinal tract. However, many diseases and therapeutics can alter gastrointestinal physiology and cause diarrhea. The extent of diarrhea-associated impact on drug pharmacokinetics has not been quantitatively described. To address this knowledge gap, we used a population pharmacokinetic modeling approach with data collected in a phase IIa study of matched human immunodeficiency virus (HIV)-infected adults with/without cryptosporidiosis and diarrhea to examine diarrhea-associated impact on oral clofazimine pharmacokinetics. A population pharmacokinetic model was developed with 428 plasma samples from 23 HIV-infected adults with/without Cryptosporidium infection using nonlinear mixed-effects modeling. Covariates describing cryptosporidiosis-associated diarrhea severity (e.g., number of diarrhea episodes, diarrhea grade) or HIV infection (e.g., viral load, CD4+ T cell count) were evaluated. A two-compartment model with lag time and first-order absorption and elimination best fit the data. Maximum diarrhea grade over the study duration was found to be associated with a more than sixfold reduction in clofazimine bioavailability. Apparent clofazimine clearance, intercompartmental clearance, central volume of distribution, and peripheral volume of distribution were 3.71 L/h, 18.2 L/h (interindividual variability [IIV] 45.0%), 473 L (IIV 3.46%), and 3434 L, respectively. The absorption rate constant was 0.625 h-1 (IIV 149%) and absorption lag time was 1.83 h. In conclusion, the maximum diarrhea grade observed for the duration of oral clofazimine administration was associated with a significant reduction in clofazimine bioavailability. Our results highlight the importance of studying disease impacts on oral therapeutic pharmacokinetics to inform dose optimization and maximize the chance of treatment success.

Human Visual Cortex and Deep Convolutional Neural Network Care Deeply about Object Background.

Deep convolutional neural networks (DCNNs) are able to partially predict brain activity during object categorization tasks, but factors contributing to this predictive power are not fully understood. Our study aimed to investigate the factors contributing to the predictive power of DCNNs in object categorization tasks. We compared the activity of four DCNN architectures with EEG recordings obtained from 62 human participants during an object categorization task. Previous physiological studies on object categorization have highlighted the importance of figure-ground segregation-the ability to distinguish objects from their backgrounds. Therefore, we investigated whether figure-ground segregation could explain the predictive power of DCNNs. Using a stimulus set consisting of identical target objects embedded in different backgrounds, we examined the influence of object background versus object category within both EEG and DCNN activity. Crucially, the recombination of naturalistic objects and experimentally controlled backgrounds creates a challenging and naturalistic task, while retaining experimental control. Our results showed that early EEG activity (< 100 msec) and early DCNN layers represent object background rather than object category. We also found that the ability of DCNNs to predict EEG activity is primarily influenced by how both systems process object backgrounds, rather than object categories. We demonstrated the role of figure-ground segregation as a potential prerequisite for recognition of object features, by contrasting the activations of trained and untrained (i.e., random weights) DCNNs. These findings suggest that both human visual cortex and DCNNs prioritize the segregation of object backgrounds and target objects to perform object categorization. Altogether, our study provides new insights into the mechanisms underlying object categorization as we demonstrated that both human visual cortex and DCNNs care deeply about object background.

Variability and patterns in children's media use and links with language development.

AIM: Media use is widespread and rising, but how often and for what purpose young children use media varies, which has differential impacts on development. Yet little work has measured how and why children under 36 months use digital media or media's consequences for language. METHODS: The current study measures how and why 17- to 30-month-old children use digital media and associations with their language abilities. The amount of use, type of activity and caregiver reasons for children's media use were then compared to the child's vocabulary and mean length of utterance. RESULTS: About 17- to 30-month-old children are primarily exposed to TV/videos over other media forms, which are often used to occupy children. Video time (but not other activities) negatively predicts children's language. The negative impact of videos on vocabulary persists regardless of the reason for use, however, the effect of videos on expressive language may be tempered when videos are used for education or connecting. CONCLUSION: Children under 36 months are using digital media at high rates. What they are doing and why they use media is critical to predict the effect it might have on language development. Further research and anticipatory guidance on the consequences of videos are needed.

Comparing apples to manzanas and oranges to naranjas: A new measure of English-Spanish vocabulary for dual language learners.

The valid assessment of vocabulary development in dual-language-learning infants is critical to developmental science. We developed the Dual Language Learners English-Spanish (DLL-ES) Inventories to measure vocabularies of U.S. English-Spanish DLLs. The inventories provide translation equivalents for all Spanish and English items on Communicative Development Inventory (CDI) short forms; extended inventories based on CDI long forms; and Spanish language-variety options. Item-Response Theory analyses applied to Wordbank and Web-CDI data (n = 2603, 12-18 months; n = 6722, 16-36 months; half female; 1% Asian, 3% Black, 2% Hispanic, 30% White, 64% unknown) showed near-perfect associations between DLL-ES and CDI long-form scores. Interviews with 10 Hispanic mothers of 18- to 24-month-olds (2 White, 1 Black, 7 multi-racial; 6 female) provide a proof of concept for the value of the DLL-ES for assessing the vocabularies of DLLs.