The physical and evolutionary energy landscapes of devolved protein sequences corresponding to pseudogenes.

Protein evolution is guided by structural, functional, and dynamical constraints ensuring organismal viability. Pseudogenes are genomic sequences identified in many eukaryotes that lack translational activity due to sequence degradation and thus over time have undergone "devolution." Previously pseudogenized genes sometimes regain their protein-coding function, suggesting they may still encode robust folding energy landscapes despite multiple mutations. We study both the physical folding landscapes of protein sequences corresponding to human pseudogenes using the Associative Memory, Water Mediated, Structure and Energy Model, and the evolutionary energy landscapes obtained using direct coupling analysis (DCA) on their parent protein families. We found that generally mutations that have occurred in pseudogene sequences have disrupted their native global network of stabilizing residue interactions, making it harder for them to fold if they were translated. In some cases, however, energetic frustration has apparently decreased when the functional constraints were removed. We analyzed this unexpected situation for Cyclophilin A, Profilin-1, and Small Ubiquitin-like Modifier 2 Protein. Our analysis reveals that when such mutations in the pseudogene ultimately stabilize folding, at the same time, they likely alter the pseudogenes' former biological activity, as estimated by DCA. We localize most of these stabilizing mutations generally to normally frustrated regions required for binding to other partners.

Machine learning in biological physics: From biomolecular prediction to design.

Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics. However, in this perspective, we argue that a more successful approach is a proper combination of these two methodologies. We discuss how ideas coming from physical modeling neuronal processing led to early formulations of computational neural networks, e.g., Hopfield networks. We then show how modern learning approaches like Potts models, Boltzmann machines, and the transformer architecture are related to each other, specifically, through a shared energy representation. We summarize recent efforts to establish these connections and provide examples on how each of these formulations integrating physical modeling and machine learning have been successful in tackling recent problems in biomolecular structure, dynamics, function, evolution, and design. Instances include protein structure prediction; improvement in computational complexity and accuracy of molecular dynamics simulations; better inference of the effects of mutations in proteins leading to improved evolutionary modeling and finally how machine learning is revolutionizing protein engineering and design. Going beyond naturally existing protein sequences, a connection to protein design is discussed where synthetic sequences are able to fold to naturally occurring motifs driven by a model rooted in physical principles. We show that this model is "learnable" and propose its future use in the generation of unique sequences that can fold into a target structure.

Maternal pre-pregnancy BMI influences the associations between bisphenol and phthalate exposures and maternal weight changes and fat accumulation.

BACKGROUND: Bisphenols and phthalates are two classes of endocrine-disrupting chemicals (EDCs) thought to influence weight and adiposity. Limited research has investigated their influence on maternal weight changes, and no prior work has examined maternal fat mass. We examined the associations between exposure to these chemicals during pregnancy and multiple maternal weight and fat mass outcomes. METHODS: This study included a sample of 318 women enrolled in a Canadian prospective pregnancy cohort. Second trimester urinary concentrations of 2 bisphenols and 12 phthalate metabolites were quantified. Self-reported and measured maternal weights and measured skinfold thicknesses were used to calculate gestational weight gain, 3-months and 3- to 5-years postpartum weight retention, late pregnancy fat mass gain, total postpartum fat mass loss, and late postpartum fat mass retention. Adjusted robust regressions examined associations between chemicals and outcomes in the entire study population and sub-groups stratified by pre-pregnancy body mass index (BMI). Bayesian kernel machine regression examined chemical mixture effects. RESULTS: Among women with underweight or normal pre-pregnancy BMIs, MBzP was negatively associated with weight retention at 3- to 5-years postpartum (B = -0.04, 95%CI: -0.07, -0.01). Among women with overweight or obese pre-pregnancy BMIs, MEHP and MMP were positively associated with weight retention at 3-months and 3- to 5-years postpartum, respectively (B's = 0.12 to 0.63, 95%CIs: 0.02, 1.07). DEHP metabolites and MCNP were positively associated with late pregnancy fat mass gain and late postpartum fat mass retention (B's = 0.04 to 0.18, 95%CIs: 0.001, 0.32). Further, the mixture of EDCs was positively associated with late pregnancy fat mass gain. CONCLUSION: In this cohort, pre-pregnancy BMI was a key determinant of the associations between second trimester exposure to bisphenols and phthalates and maternal weight changes and fat accumulation. Investigations of underlying physiological mechanisms, windows of susceptibility, and impacts on maternal and infant health are needed.

Utilizing generative conversational artificial intelligence to create simulated patient encounters: a pilot study for anaesthesia training.

PURPOSE OF THE STUDY: Generative conversational artificial intelligence (AI) has huge potential to improve medical education. This pilot study evaluated the possibility of using a 'no-code' generative AI solution to create 2D and 3D virtual avatars, that trainee doctors can interact with to simulate patient encounters. METHODS: The platform 'Convai' was used to create a virtual patient avatar, with a custom backstory, to test the feasibility of this technique. The virtual patient model was set up to allow trainee anaesthetists to practice answering questions that patients' may have about interscalene nerve blocks for open reduction and internal fixation surgery. This tool was provided to anaesthetists to receive their feedback and evaluate the feasibility of this approach. RESULTS: Fifteen anaesthetists were surveyed after using the tool. The tool had a median score [interquartile range (IQR)] of 9 [7-10] in terms of how intuitive and user-friendly it was, and 8 [7-10] in terms of accuracy in simulating patient responses and behaviour. Eighty-seven percent of respondents felt comfortable using the model. CONCLUSIONS: By providing trainees with realistic scenarios, this technology allows trainees to practice answering patient questions regardless of actor availability, and indeed from home. Furthermore, the use of a 'no-code' platform allows clinicians to create customized training tools tailored to their medical specialties. While overall successful, this pilot study highlighted some of the current drawbacks and limitations of generative conversational AI, including the risk of outputting false information. Additional research and fine-tuning are required before generative conversational AI tools can act as a substitute for actors and peers.

Bypassing the Identification: MS2Quant for Concentration Estimations of Chemicals Detected with Nontarget LC-HRMS from MS2 Data.

Nontarget analysis by liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is now widely used to detect pollutants in the environment. Shifting away from targeted methods has led to detection of previously unseen chemicals, and assessing the risk posed by these newly detected chemicals is an important challenge. Assessing exposure and toxicity of chemicals detected with nontarget HRMS is highly dependent on the knowledge of the structure of the chemical. However, the majority of features detected in nontarget screening remain unidentified and therefore the risk assessment with conventional tools is hampered. Here, we developed MS2Quant, a machine learning model that enables prediction of concentration from fragmentation (MS2) spectra of detected, but unidentified chemicals. MS2Quant is an xgbTree algorithm-based regression model developed using ionization efficiency data for 1191 unique chemicals that spans 8 orders of magnitude. The ionization efficiency values are predicted from structural fingerprints that can be computed from the SMILES notation of the identified chemicals or from MS2 spectra of unidentified chemicals using SIRIUS+CSI:FingerID software. The root mean square errors of the training and test sets were 0.55 (3.5×) and 0.80 (6.3×) log-units, respectively. In comparison, ionization efficiency prediction approaches that depend on assigning an unequivocal structure typically yield errors from 2× to 6×. The MS2Quant quantification model was validated on a set of 39 environmental pollutants and resulted in a mean prediction error of 7.4×, a geometric mean of 4.5×, and a median of 4.0×. For comparison, a model based on PaDEL descriptors that depends on unequivocal structural assignment was developed using the same dataset. The latter approach yielded a comparable mean prediction error of 9.5×, a geometric mean of 5.6×, and a median of 5.2× on the validation set chemicals when the top structural assignment was used as input. This confirms that MS2Quant enables to extract exposure information for unidentified chemicals which, although detected, have thus far been disregarded due to lack of accurate tools for quantification. The MS2Quant model is available as an R-package in GitHub for improving discovery and monitoring of potentially hazardous environmental pollutants with nontarget screening.

Evaluating atezolizumab in patients with urinary tract squamous cell carcinoma (AURORA): study protocol for a single arm, open-label, multicentre, phase II clinical trial.

BACKGROUND: Bladder and urinary tract cancers account for approximately 21,000 new diagnoses and 5,000 deaths annually in the UK. Approximately 90% are transitional cell carcinomas where advanced disease is treated with platinum based chemotherapy and PD-1/PD-L1 directed immunotherapy. Urinary tract squamous cell carcinoma (UTSCC) accounts for about 5% of urinary tract cancers overall making this a rare disease. We have yet to establish definitive systemic treatment options for advanced UTSCC. Preliminary translational data, from UTSCC patient tumour samples, indicate high PD-L1 expression and tumour infiltrating lymphocytes in a proportion of cases. Both of these features are associated with differential gene expression consistent with a tumour/immune microenvironment predicted to be susceptible to immune checkpoint directed immunotherapy which we will evaluate in the AURORA trial. METHODS: AURORA is a single arm, open-label, multicentre,UK phase II clinical trial. 33 patients will be recruited from UK secondary care sites. Patients with UTSCC, suitable for treatment with palliative intent, will receive atezolizumab PD-L1 directed immunotherapy (IV infusion, 1680 mg, every 28 days) for one year if tolerated. Response assessment, by cross sectional imaging will occur every 12 weeks. AURORA uses a Simon's 2-stage optimal design with best overall objective response rate (ORR, by RECIST v1.1) at a minimum of 12 weeks from commencing treatment as the primary endpoint. Secondary endpoints will include overall survival, progression-free survival, duration of response, magnitude of response using waterfall plots of target lesion measurements, quality of life using the EORTC QLQ-C30 tool, safety and tolerability (CTCAE v5) and evaluation of potential biomarkers of treatment response including PD-L1 expression. Archival tumour samples and blood samples will be collected for translational analyses. DISCUSSION: If this trial shows atezolizumab to be safe and effective it may lead to a future late phase randomised controlled trial in UTSCC. Ultimately, we hope to provide a new option for treatment for such patients. TRIAL REGISTRATIONS: EudraCT Number: 2021-001995-32 (issued 8th September 2021); ISRCTN83474167 (registered 11 May 2022); NCT05038657 (issued 9th September 2021).

Intravascular Ultrasound-Guided Transvenous Biopsy of Retroperitoneal Lymph Nodes: Diagnostic Accuracy and Safety Compared with CT-Guided Percutaneous Biopsy.

PURPOSE: To compare the diagnostic accuracy and adverse event rates of intravascular ultrasound (US)-guided transvenous biopsy (TVB) versus those of computed tomography (CT)-guided percutaneous needle biopsy (PNB) for retroperitoneal (RP) lymph nodes. MATERIALS AND METHODS: In this single-institution, retrospective study, 32 intravascular US-guided TVB procedures and a sample of 34 CT-guided PNB procedures for RP lymph nodes where targets were deemed amenable to intravascular US-guided TVB were analyzed. Procedural metrics, including diagnostic accuracy, defined as diagnostic of malignancy or a clinically verifiable benign result, and adverse event rates were compared. RESULTS: The targets of intravascular US-guided TVB were primarily aortocaval (47%, 15/32) or precaval (34%, 11/32), whereas those of CT-guided PNB were primarily right pericaval (44%, 15/34) or retrocaval (44%, 15/34) (P < .001). The targets of intravascular US-guided TVB averaged 2.4 cm in the long axis (range, 1.3-3.7 cm) compared with 2.9 cm (range, 1.4-5.7 cm) for those of CT-guided PNB (P = .02). There was no difference in the average number of needle passes (3.8 for intravascular US-guided TVB vs 3.9 for CT-guided PNB; P = .68). The diagnostic accuracy was 94% (30/32) and the adverse event rate was 3.1% (1/32) for intravascular US-guided TVB, similar to those of CT-guided PNB (accuracy, 91% [31/34]; adverse event rate, 2.9% [1/34]). CONCLUSIONS: Intravascular US-guided TVB had a diagnostic accuracy and adverse event rate similar to CT-guided PNB for RP lymph nodes, indicating that intravascular US-guided TVB may be as safe and effective as conventional biopsy approaches for appropriately selected targets.

Intravascular Ultrasound Guidance for Transjugular Intrahepatic Portosystemic Shunt Creation Reduces Laboratory Markers of Acute Liver Injury.

PURPOSE: To compare the laboratory markers of acute liver injury after transjugular intrahepatic portosystemic shunt (TIPS) creation performed using intravascular ultrasound (IVUS) guidance with those using other techniques. MATERIALS AND METHODS: This single-center, retrospective study examined 293 TIPS procedures performed between 2014 and 2022 (160 men; mean age, 57.4 years; 71.7% with ascites, 158 with IVUS). Laboratory changes on postprocedural day (PPD) 1 were classified based on Common Terminology Criteria for Adverse Events (CTCAE) grades and were compared between IVUS and non-IVUS cases. RESULTS: IVUS cases had a lower baseline Model for End-Stage Liver Disease (MELD) score (12.5 vs 13.7, P = .016), higher pre- (16.8 vs 15.2, P = .009), and post-TIPS (6.6 vs 5.4 mm Hg, P < .001) pressure gradient, smaller stent diameter (9.2 vs 9.9 mm, P < .001), and fewer needle passes (2.4 vs 4.2, P < .001). IVUS predicted a lower PPD 1 CTCAE grade for aspartate transaminase (8.0% vs 22.2% grade ≥ 2, P = .010), alanine transaminase (ALT) (2.2% vs 7.1%, P = .017), and bilirubin (9.4% vs 26.2%, P < .001), findings confirmed using multivariable regression and propensity score analysis. IVUS predicted fewer adverse events (1.3% vs 8.1%, P = .008) and an increased likelihood of PPD 1 discharge (81% vs 59%, P = .004). IVUS was not associated with differences in PPD 30 MELD scores or 30-day survival; however, higher PPD 1 ALT (ß = 1.96, P = .008) and bilirubin levels (ß = 1.38, P = .004) predicted larger PPD 30 MELD score increase. Higher increases in ALT level predicted worse 30-day survival (hazard ratio, 1.93; P = .021). CONCLUSION: IVUS resulted in less laboratory evidence of acute liver injury immediately following TIPS creation.

Phospholipid Removal for Enhanced Chemical Exposomics in Human Plasma.

The challenge of chemical exposomics in human plasma is the 1000-fold concentration gap between endogenous substances and environmental pollutants. Phospholipids are the major endogenous small molecules in plasma, thus we validated a chemical exposomics protocol with an optimized phospholipid-removal step prior to targeted and non-targeted liquid chromatography high-resolution mass spectrometry. Increased injection volume with negligible matrix effect permitted sensitive multiclass targeted analysis of 77 priority analytes; median MLOQ = 0.05 ng/mL for 200 µL plasma. In non-targeted acquisition, mean total signal intensities of non-phospholipids were enhanced 6-fold in positive (max 28-fold) and 4-fold in negative mode (max 58-fold) compared to a control method without phospholipid removal. Moreover, 109 and 28% more non-phospholipid molecular features were detected by exposomics in positive and negative mode, respectively, allowing new substances to be annotated that were non-detectable without phospholipid removal. In individual adult plasma (100 µL, n = 34), 28 analytes were detected and quantified among 10 chemical classes, and quantitation of per- and polyfluoroalkyl substances (PFAS) was externally validated by independent targeted analysis. Retrospective discovery and semi-quantification of PFAS-precursors was demonstrated, and widespread fenuron exposure is reported in plasma for the first time. The new exposomics method is complementary to metabolomics protocols, relies on open science resources, and can be scaled to support large studies of the exposome.

Nontarget Analysis of Polluted Surface Waters in Bangladesh Using Open Science Workflows.

Nontarget mass spectrometry has great potential to reveal patterns of water contamination globally through community science, but few studies are conducted in low-income countries, nor with open-source workflows, and few datasets are FAIR (Findable, Accessible, Interoperable, Reusable). Water was collected from urban and rural rivers around Dhaka, Bangladesh, and analyzed by liquid chromatography high-resolution mass spectrometry in four ionization modes (electrospray ionization ±, atmospheric pressure chemical ionization ±) with data-independent MS2 acquisition. The acquisition strategy was complementary: 19,427 and 7365 features were unique to ESI and APCI, respectively. The complexity of water pollution was revealed by >26,000 unique molecular features resolved by MS-DIAL, among which >20,000 correlated with urban sources in Dhaka. A major wastewater treatment plant was not a dominant pollution source, consistent with major contributions from uncontrolled urban drainage, a result that encourages development of further wastewater infrastructures. Matching of deconvoluted MS2 spectra to public libraries resulted in 62 confident annotations (i.e., Level 1-2a) and allowed semiquantification of 42 analytes including pharmaceuticals, pesticides, and personal care products. In silico structure prediction for the top 100 unknown molecular features associated with an urban source allowed 15 additional chemicals of anthropogenic origin to be annotated (i.e., Level 3). The authentic MS2 spectra were uploaded to MassBank Europe, mass spectral data were openly shared on the MassIVE repository, a tool (i.e., MASST) that could be used for community science environmental surveillance was demonstrated, and current limitations were discussed.

Novel Per- and Polyfluoroalkyl Substances Discovered in Cattle Exposed to AFFF-Impacted Groundwater.

The leaching of per- and polyfluoroalkyl substances (PFASs) from Australian firefighting training grounds has resulted in extensive contamination of groundwater and nearby farmlands. Humans, farm animals, and wildlife in these areas may have been exposed to complex mixtures of PFASs from aqueous film-forming foams (AFFFs). This study aimed to identify PFAS classes in pooled whole blood (n = 4) and serum (n = 4) from cattle exposed to AFFF-impacted groundwater and potentially discover new PFASs in blood. Thirty PFASs were identified at various levels of confidence (levels 1a-5a), including three novel compounds: (i) perfluorohexanesulfonamido 2-hydroxypropanoic acid (FHxSA-HOPrA), (ii) methyl((perfluorohexyl)sulfonyl)sulfuramidous acid, and (iii) methyl((perfluorooctyl)sulfonyl)sulfuramidous acid, belonging to two different classes. Biotransformation intermediate, perfluorohexanesulfonamido propanoic acid (FHxSA-PrA), hitherto unreported in biological samples, was detected in both whole blood and serum. Furthermore, perfluoroalkyl sulfonamides, including perfluoropropane sulfonamide (FPrSA), perfluorobutane sulfonamide (FBSA), and perfluorohexane sulfonamide (FHxSA) were predominantly detected in whole blood, suggesting that these accumulate in the cell fraction of blood. The suspect screening revealed several fluoroalkyl chain-substituted PFAS. The results suggest that targeting only the major PFASs in the plasma or serum of AFFF-exposed mammals likely underestimates the toxicological risks associated with exposure. Future studies of AFFF-exposed populations should include whole-blood analysis with high-resolution mass spectrometry to understand the true extent of PFAS exposure.

Associations between the chemical exposome and pregnancy induced hypertension.

Exposure to environmental chemicals has been linked to an increased risk of pregnancy-induced hypertension (PIH). This prospective cohort study examined the associations between PIH and maternal chemical exposure to four classes of chemicals (i.e., phthalates, bisphenols, perfluoroalkyl acids, non-essential metals and trace minerals). Participants included 420 pregnant women from the Alberta Pregnancy Outcomes and Nutrition (APrON) cohort who had data available on diagnosed PIH and environmental chemical exposure. Twelve phthalate metabolites, two bisphenols, eight perfluoroalkyl acids and eleven non-essential metals or trace minerals were quantified in maternal urine or blood samples collected in the second trimester of pregnancy. Associations between the urinary and blood concentrations of these chemicals and PIH were assessed using multiple logistic and LASSO regression analyses in single- and multi-chemical exposure models, respectively. Thirty-five (8.3%) participants were diagnosed with PIH. In single chemical exposure models, two phthalate metabolites, mono-methyl phthalate (MMP) and monoethyl phthalate (MEP), three perfluoroalkyl acids, perfluoroheptanoic acid (PFHpA), perfluorononanoic acid (PFNA), and perfluorodecanoic acid (PFDA), and one metal, manganese, were associated with increased odds of PIH. The metabolites of di (2-ethylhexyl) phthalate (DEHP) and the molar sum of these metabolites, as well as antimony, displayed trend associations (p < 0.10). In multi-chemical exposure models using LASSO penalized regressions and double-LASSO regressions, MEP (AOR: 1.43, 95% CI: 1.09-1.88, p = 0.009) and PFNA (AOR: 2.03, 95% CI: 1.01-4.07, p = 0.04) were selected as the chemicals most highly associated with PIH. These findings suggest that maternal levels of phthalates and perfluoroalkyl acids may be associated with the diagnosis on PIH. Future research should consider both individual and multi-chemical exposures when examining predictors of PIH and other maternal cardiometabolic health disorders, such as preeclampsia, eclampsia, HELLP syndrome, and gestational diabetes.

Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species.

In medical, veterinary and forensic entomology, the ease and affordability of image data acquisition have resulted in whole-image analysis becoming an invaluable approach for species identification. Krawtchouk moment invariants are a classical mathematical transformation that can extract local features from an image, thus allowing subtle species-specific biological variations to be accentuated for subsequent analyses. We extracted Krawtchouk moment invariant features from binarised wing images of 759 male fly specimens from the Calliphoridae, Sarcophagidae and Muscidae families (13 species and a species variant). Subsequently, we trained the Generalized, Unbiased, Interaction Detection and Estimation random forests classifier using linear discriminants derived from these features and inferred the species identity of specimens from the test samples. Fivefold cross-validation results show a 98.56 ± 0.38% (standard error) mean identification accuracy at the family level and a 91.04 ± 1.33% mean identification accuracy at the species level. The mean F1-score of 0.89 ± 0.02 reflects good balance of precision and recall properties of the model. The present study consolidates findings from previous small pilot studies of the usefulness of wing venation patterns for inferring species identities. Thus, the stage is set for the development of a mature data analytic ecosystem for routine computer image-based identification of fly species that are of medical, veterinary and forensic importance.

Incorporation of a biparietal narrowing metric to improve the ability of machine learning models to detect sagittal craniosynostosis with 2D photographs.

OBJECTIVE: Sagittal craniosynostosis is the most common form of craniosynostosis and typically results in scaphocephaly, which is characterized by biparietal narrowing, compensatory frontal bossing, and an occipital prominence. The cephalic index (CI) is a simple metric for quantifying the degree of cranial narrowing and is often used to diagnose sagittal craniosynostosis. However, patients with variant forms of sagittal craniosynostosis may present with a "normal" CI, depending on the part of the suture that is closed. As machine learning (ML) algorithms are developed to assist in the diagnosis of cranial deformities, metrics that reflect the other phenotypic features of sagittal craniosynostosis are needed. In this study the authors sought to describe the posterior arc angle (PAA), a measurement of biparietal narrowing that is obtained with 2D photographs, and elucidate the role of PAA as an adjuvant to the CI in characterizing scaphocephaly and the potential relevance of PAA in new ML model development. METHODS: The authors retrospectively reviewed 1013 craniofacial patients treated during the period from 2006 to 2021. Orthogonal top-down photographs were used to calculate the CI and PAA. Distribution densities, receiver operating characteristic (ROC) curves, and chi-square analyses were used to describe the relative predictive utility of each method for sagittal craniosynostosis. RESULTS: In total, 1001 patients underwent paired CI and PAA measurements and a clinical head shape diagnosis (sagittal craniosynostosis, n = 122; other cranial deformity, n = 565; normocephalic, n = 314). The area under the ROC curve (AUC) for the CI was 98.5% (95% confidence interval 97.8%-99.2%, p < 0.001), with an optimum specificity of 92.6% and sensitivity of 93.4%. The PAA had an AUC of 97.4% (95% confidence interval 96.0%-98.8%, p < 0.001) with an optimum specificity of 94.9% and sensitivity of 90.2%. In 6 of 122 (4.9%) cases of sagittal craniosynostosis, the PAA was abnormal while the CI was normal. This means that adding a PAA cutoff branch to a partition model increases the detection of sagittal craniosynostosis. CONCLUSIONS: Both CI and PAA are excellent discriminators for sagittal craniosynostosis. Using an accuracy-optimized partition model, the addition of the PAA to the CI increased model sensitivity compared to using the CI alone. Using a model that incorporates both CI and PAA could assist in the early identification and treatment of sagittal craniosynostosis via automated and semiautomated algorithms that utilize tree-based ML models.

Anticoagulation for the treatment of septic cerebral venous sinus thrombosis in the setting of pediatric sinogenic and otogenic intracranial infections.

OBJECTIVE: Septic cerebral venous sinus thrombosis (CVST) is a recognized complication of pediatric sinogenic and otogenic intracranial infections. The optimal treatment paradigm remains controversial. Proponents of anticoagulation highlight its role in preventing thrombus propagation and promoting recanalization, while others cite the risk of hemorrhagic complications, especially after a neurosurgical procedure for an epidural abscess or subdural empyema. Here, the authors investigated the diagnosis, management, and outcomes of pediatric patients with sinogenic or otogenic intracranial infections and a septic CVST. METHODS: All patients 21 years of age or younger, who presented with an intracranial infection in the setting of sinusitis or otitis media and who underwent neurosurgical treatment at Connecticut Children's, Rady Children's Hospital-San Diego, or Ann and Robert H. Lurie Children's Hospital of Chicago from March 2015 to March 2023, were retrospectively reviewed. Demographic, clinical, and radiological data were systematically collated. RESULTS: Ninety-six patients were treated for sinusitis-related and/or otitis media-related intracranial infections during the study period, 15 (15.6%) of whom were diagnosed with a CVST. Of the 60 patients who presented prior to the COVID-19 pandemic, 6 (10.0%) were diagnosed with a septic CVST, whereas of the 36 who presented during the COVID-19 pandemic, 9 (25.0%) had a septic CVST (p = 0.050). The superior sagittal sinus was involved in 12 (80.0%) patients and the transverse and/or sigmoid sinuses in 4 (26.7%). Only 1 (6.7%) patient had a fully occlusive thrombus. Of the 15 patients with a septic CVST, 11 (73.3%) were initiated on anticoagulation at a median interval of 4 (IQR 3-5) days from the most recent neurosurgical procedure. Five (45.5%) patients who underwent anticoagulation demonstrated complete recanalization on follow-up imaging, and 4 (36.4%) had partial recanalization. Three (75.0%) patients who did not undergo anticoagulation demonstrated complete recanalization, and 1 (25.0%) had partial recanalization. None of the patients treated with anticoagulation experienced hemorrhagic complications. CONCLUSIONS: Septic CVST is frequently identified among pediatric patients undergoing neurosurgical intervention for sinogenic and/or otogenic intracranial infections and may have become more prevalent during the COVID-19 pandemic. Anticoagulation can be used safely in the acute postoperative period if administered cautiously, in a monitored setting, and with interval cross-sectional imaging. However, some patients exhibit excellent outcomes without anticoagulation, and further studies are needed to identify those who may benefit the most from anticoagulation.

MicroRNA Dysregulation in Early Breast Cancer Diagnosis: A Systematic Review and Meta-Analysis.

Breast cancer continues to be the leading cause of death in women worldwide. Mammography, which is the current gold standard technique used to diagnose it, presents strong limitations in early ages where breast cancer is much more aggressive and fatal. MiRNAs present in numerous body fluids might represent a new line of research in breast cancer biomarkers, especially oncomiRNAs, known to play an important role in the suppression and development of neoplasms. The aim of this systematic review and meta-analysis was to evaluate dysregulated miRNA biomarkers and their diagnostic accuracy in breast cancer. Two independent researchers reviewed the included studies according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. A protocol for this review was registered in PROSPERO with the registration number "CRD42021256338". Observational case-control-based studies analyzing concentrations of microRNAs which have been published within the last 10 years were selected, and the concentrations of miRNAs in women with breast cancer and healthy controls were analyzed. Random-effects meta-analyses of miR-155 were performed on the studies which provided enough data to calculate diagnostic odds ratios. We determined that 34 microRNAs were substantially dysregulated and could be considered biomarkers of breast cancer. Individually, miR-155 provided better diagnostic results than mammography on average. However, when several miRNAs are used to screen, forming a panel, sensitivity and specificity rates improve, and they can be associated with classic biomarkers such us CA-125 or CEA. Based on the results of our meta-analysis, miR-155 might be a promising diagnostic biomarker for this patient population.

Complex impacts of hydraulic fracturing return fluids on soil microbial community respiration, structure and functional potentials.

The consequences of soils exposed to hydraulic fracturing (HF) return fluid, often collectively termed flowback and produced water (FPW), are poorly understood, even though soils are a common receptor of FPW spills. Here, we investigate the impacts on soil microbiota exposed to FPW collected from the Montney Formation of western Canada. We measured soil respiration, microbial community structure and functional potentials under FPW exposure across a range of concentrations, exposure time and soil types (luvisol and chernozem). We find that soil type governs microbial community response upon FPW exposure. Within each soil, FPW exposure led to reduced biotic soil respiration, and shifted microbial community structure and functional potentials. We detect substantially higher species richness and more unique functional genes in FPW-exposed soils than in FPW-unexposed soils, with metagenome-assembled genomes (e.g. Marinobacter persicus) from luvisol soil exposed to concentrated FPW being most similar to genomes from HF/FPW sites. Our data demonstrate the complex impacts of microbial communities following FPW exposure and highlight the site-specific effects in evaluation of spills and agricultural reuse of FPW on the normal soil functions.

Selective excitation localized by the Bloch-Siegert shift and a B1+ gradient.

PURPOSE: To perform B1+$$ {B}_1^{+} $$ -selective excitation using the Bloch-Siegert shift for spatial localization. THEORY AND METHODS: A B1+$$ {B}_1^{+} $$ -selective excitation is produced by an radiofrequency (RF) pulse consisting of two summed component pulses: an off-resonant pulse that induces a B1+$$ {B}_1^{+} $$ -dependent Bloch-Siegert frequency shift and a frequency-selective excitation pulse. The passband of the pulse can be tailored by adjusting the frequency content of the frequency-selective pulse, as in conventional B0$$ {B}_0 $$ gradient-localized excitation. Fine magnetization profile control is achieved by using the Shinnar-Le Roux algorithm to design the frequency-selective excitation pulse. Simulations analyzed the pulses' robustness to off-resonance, their suitability for multi-echo spin echo pulse sequences, and how their performance compares to that of rotating-frame selective excitation pulses. The pulses were evaluated experimentally on a 47.5 mT MRI scanner using an RF gradient transmit coil. Multiphoton resonances produced by the pulses were characterized and their distribution across B1+$$ {B}_1^{+} $$ predicted. RESULTS: With correction for varying B1+$$ {B}_1^{+} $$ across the desired profile, the proposed pulses produced selective excitation with the specified profile characteristics. The pulses were robust against off-resonance and RF amplifier distortion, and suitable for multi-echo pulse sequences. Experimental profiles closely matched simulated patterns. CONCLUSION: The Bloch-Siegert shift can be used to perform B0$$ {B}_0 $$ -gradient-free selective excitation, enabling the excitation of slices or slabs in RF gradient-encoded MRI.

A machine-learning approach to predict success of a biocontrol for invasive Eurasian watermilfoil reduction.

Myriophyllum spicatum, more commonly known as Eurasian watermilfoil (EWM), is one of the most invasive aquatic plants in North America, causing negative ecological and economic impacts in ecosystems where it proliferates. Many control strategies have been developed and implemented to mitigate EWM growth and spread, although the results are mixed and there is no consensus on lake-specific strategies. Here, we describe the development of a predictive model using a support vector technique, that predicts the success of biological pest control using Euhrychiopsis lecontei (the milfoil weevil), a milfoil specialist, to reduce EWM in lakes. Such a model is informed by lake characteristics (limnological and landscape) and augmentation strategies. To develop our predictive model, we performed a metadata analysis from 133 published peer-reviewed literature and professional reports of milfoil weevil augmentation field experiments that contained information on lake characteristics. The predictive model's algorithm uses a support vector machine (SMV) to learn patterns among lake characteristics, along with the recorded augmentation strategy and the reported success of each study, where success is a measure of EWM change over a season and is recorded in a variety of ways (e.g., EWM biomass change, EWM percent change, EWM visual change, etc.,). Overall, the model results suggests that shallower lakes, more frequent weevil augmentations, and larger weevil overwintering habitat are the most important predictors for EWM reduction success by weevil augmentation. Although watermilfoil weevil augmentation is a promising mitigation strategy, it may not work for all lakes. However, in terms of suggesting weevil augmentation, our model is a valuable tool for lake stakeholders and resource managers, who can use it to determine whether milfoil weevil augmentation, which can be very costly due to the difficulties in finding and raising milfoil weevils, will be a useful and sustainable approach to control EWM in their lake community.

Safety Profile of Particle Embolization for Treatment of Acute Lower Gastrointestinal Bleeding.

PURPOSE: To assess ischemic adverse events following particle embolization when used as a second-line embolic to coil embolization for the treatment of acute lower gastrointestinal bleeding (LGIB). MATERIALS AND METHODS: The single-institution retrospective study examined 154 procedures where embolization was attempted for LGIB. In 122 patients (64 men; mean age, 69.9 years), embolization was successfully performed using microcoils in 73 procedures, particles in 34 procedures, and both microcoils and particles in 27 procedures. Particles were used as second-line only when coil embolization was infeasible or inadequate. Technical success was defined as angiographic cessation of active extravasation after embolization. Clinical success was defined as the absence of recurrent bleeding within 30 days of embolization. RESULTS: Technical success for embolization of LGIB was achieved in 87% of the cases (134/154); clinical success rate was 76.1% (102/134) among the technically successful cases. Clinical success was 82.2% (60/73) for coils alone and 68.9% (42/61) for particles with or without coils. Severe adverse events involving embolization-induced bowel ischemia occurred in 3 of 56 (5.3%) patients who underwent particle embolization with or without coils versus zero of 66 patients when coils alone were used (P = .09). In patients who had colonoscopy or bowel resection within 2 weeks of embolization, ischemic findings attributable to the procedure were found in 3 of the 15 who underwent embolization with coils alone versus 8 of 18 who underwent embolization with particles with or without coils (P = .27). CONCLUSIONS: Particle embolization for the treatment of LGIB as second-line to coil embolization was associated with a 68.9% clinical success rate and a 5.3% rate of ischemia-related adverse events.