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The quantification of subcortical volume development from 3D fetal ultrasound can provide important diagnostic information during pregnancy monitoring. However, manual segmentation of subcortical structures in ultrasound volumes is time-consuming and challenging due to low soft tissue contrast, speckle and shadowing artifacts. For this reason, we developed a convolutional neural network (CNN) for the automated segmentation of the choroid plexus (CP), lateral posterior ventricle horns (LPVH), cavum septum pellucidum et vergae (CSPV), and cerebellum (CB) from 3D ultrasound. As ground-truth labels are scarce and expensive to obtain, we applied few-shot learning, in which only a small number of manual annotations (n = 9) are used to train a CNN. We compared training a CNN with only a few individually annotated volumes versus many weakly labelled volumes obtained from atlas-based segmentations. This showed that segmentation performance close to intra-observer variability can be obtained with only a handful of manual annotations. Finally, the trained models were applied to a large number (n = 278) of ultrasound image volumes of a diverse, healthy population, obtaining novel US-specific growth curves of the respective structures during the second trimester of gestation.

Abstract Building reliable and robust quantitative structure–property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred’s modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrates customized implementations in a “plug-and-play” manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred’s functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at https://github.com/CDDLeiden/QSPRpred . Scientific Contribution QSPRpred aims to provide a complex, but comprehensive Python API to conduct all tasks encountered in QSPR modelling from data preparation and analysis to model creation and model deployment. In contrast to similar packages, QSPRpred offers a wider and more exhaustive range of capabilities and integrations with many popular packages that also go beyond QSPR modelling. A significant contribution of QSPRpred is also in its automated and highly standardized serialization scheme, which significantly improves reproducibility and transferability of models.

Abstract Signaling pathways in fungi offer a profound avenue for harnessing cellular communication and have garnered considerable interest in biomaterial engineering. Fungi respond to environmental stimuli through intricate signaling networks involving biochemical and electrical pathways, yet deciphering these mechanisms remains a challenge. In this review, an overview of fungal biology and their signaling pathways is provided, which can be activated in response to external stimuli and direct fungal growth and orientation. By examining the hyphal structure and the pathways involved in fungal signaling, the current state of recording fungal electrophysiological signals as well as the landscape of fungal biomaterials is explored. Innovative applications are highlighted, from sustainable materials to biomonitoring systems, and an outlook on the future of harnessing fungi signaling in living composites is provided.

IntroductionThe combination of neoadjuvant immunotherapy and chemotherapy (NICT) has become a common treatment regimen for locally advanced gastric cancer (LAGC). However, the safety and efficacy of radical gastrectomy following NICT (NICT-G) remain controversial. This study aimed to analyze the risk factors influencing postoperative complications (POCs) after NICT-G. Additionally, it aimed to construct a nomogram to provide a clinical reference for predicting POCs.MethodsThis study included 177 patients who received NICT-G at the Chinese PLA General Hospital First Medical Center from January 2020 to January 2024. Univariable and multivariable logistic regression models were used to evaluate the risk factors influencing POCs, and a nomogram model was constructed. To evaluate the discrimination and accuracy of the nomogram model, the area under the receiver operating characteristic curve (AUC) and the calibration curve were measured.ResultsIn 177 patients who received NICT-G, the pathological complete response and major pathological response rates were 15.8% and 45.2%, respectively, whereas the rates of the overall and severe treatment-related adverse events were 71.8% and 15.8%, respectively. In addition, 43 (24.3%) patients developed overall POCs (Clavien–Dindo classification ≥ II). Univariable and multivariable logistic analyses showed that age ≥70 years, greater estimated blood loss, platelet/lymphocyte ratio (PLR) ≤196, neutrophil/lymphocyte ratio (NLR) >1.33, non-R0 resection, and body mass index (BMI) < 18.5 kg/m2 were independent risk factors for overall POCs (p < 0.05). The nomogram model developed using the abovementioned variables showed that the AUC (95% confidence interval [CI]) was 0.808 (95% CI): 0.731–0.885 in predicting the POC risk. The calibration curves showed that the prediction curve of the nomogram was a good fit for the actual POCs (Hosmer–Lemeshow test: χ2 = 5.76, P = 0.451).ConclusionThe independent risk factors for overall POCs in the NICT-G were age ≥ 70 years, greater estimated blood loss, PLR ≤ 196, NLR > 1.33, non-R0 resection, and BMI < 18.5 kg/m2. The nomogram model developed based on the abovementioned indicators showed better accuracy in predicting the POC risk.

Objectives: We evaluated the diagnostic accuracy of cerebrospinal fluid (CSF) inflammatory markers for diagnosing bacterial meningitis in neonates with sepsis and/or meningitis. Methods: Cases were identified from a prospective multicenter study including patients aged 0-3 months with Group B Streptococcal (GBS) or Escherichia coli culture positive sepsis/meningitis. CSF CXCL10, MDC, IL-6, IL-8, IL-10, TNF- α, MIF, IL-1RA, CXCL13, IL-1β, CRP and procalcitonin concentrations were measured with Luminex technology. Results: In 61/373 patients (17%) residual CSF from the lumbar puncture was available, of whom 16 (26%) had definitive meningitis, 15 (25%) probable meningitis and 30 (49%) had sepsis. All biomarkers were detectable in CSF and showed significantly higher concentrations in definitive meningitis versus sepsis patients and six biomarkers in probable meningitis versus sepsis patients. Discrimination between definitive meningitis and sepsis was excellent for IL-1RA (area under the receiver operating characteristic curve [AUC] 0.93), TNF-α (AUC 0.92), CXCL10 (AUC 0.90), IL-1β (AUC 0.92), IL-6 (AUC 0.94), IL-10 (AUC 0.93) and a combination of IL-1RA, TNF-α, CXCL-10 and CSF leukocyte count (AUC 0.95). CSF leukocyte count remained the predictor with the highest diagnostic accuracy (AUC 0.96). Conclusion: CSF inflammatory markers can be used to differentiate between neonatal sepsis and meningitis.

Objective. Large-scale studies on the characteristics and management of abdominal trauma in megacities in China are lacking. The aim of this study was to analyze and present the clinical patterns and treatment status of abdominal trauma in regional medical centers. Methods. Cases of abdominal trauma treated at seven medical centers in Beijing from 2010 to 2021 were collected. Clinical information about age, sex, injury cause, geographic distribution, abbreviated injury scale/injury severity score (AIS/ISS) value, injury-hospital time, preoperative time, surgically identified organ injuries, type of surgery, causes of reoperation and 90-day mortality was included in this study. Clinical characteristics, treatment methods, and short-term prognoses (90-days survival) were compared between blunt abdominal trauma (BAT) and penetrating abdominal trauma (PAT) cases. Non-normally distributed data are described as medians (IQR), and the Mann‒Whitney U test was performed; qualitative data were analyzed using the X2 test. Univariate and multivariate survival analyses were performed by the Cox proportional hazards model. Results. A total of 553 patients (86.98% male) with a median age of 36.50 (27.00–48.00) years were included. The BAT group had a significantly higher proportion of serious injury (P=0.001), lower initial hemoglobin level (P=0.001), and a lower laparoscopy surgery rate (P=0.044) compared to the PAT group. Additionally, more BAT cases were from the area around Beijing (P=0.008) and a longer injury-regional hospital time (10.47 (5.18–22.51) hours vs. 7.00 (3.80–15.38) hours, P=0.001). In the hollow viscus injury subgroup, the BAT group had a significantly longer injury-regional hospital time and preoperative time compared to the PAT group (injury-regional hospital time: 10.23 (6.00–21.59) hours vs. 7.07 (3.99–13.85) hours, P=0.002; preoperative time: 3.02 (2.01–5.58) hours vs. 2.81 (1.85–3.63) hours, P=0.047). The overall 90-day mortality was 11.9%, and longer injury-regional hospital time (HR: 1.01, 95% CI: 1.00–1.02, P=0.008), receipt of ICU treatment (HR: 4.69, 95% CI: 2.54–8.65, P=0.001), and severe ISSs (ISS > 25 vs. ISS

Objectives This study aimed to estimate the recurrence rate of culture-positive bacterial meningitis in children in the Netherlands.Design Nationwide surveillance study, using the database of the Netherlands Reference Laboratory for Bacterial Meningitis to identify patients with culture-positive bacterial meningitis during childhood.Setting The study was based in the Netherlands.Participants A total of 9731 children with a first bacterial meningitis episode between 1 July 1987 and 30 June 2019 were identified.Primary and secondary outcome measures Recurrence was defined as a subsequent episode >28 days, or caused by a different pathogen. Annual incidence and incidence rate ratios (IRRs) comparing the periods 1988–2003 and 2004–2019 were calculated. Predictors of recurrent meningitis were assessed using Cox proportional hazards regression.Results Sixty-three (0.6%) of the 9731 children with a first bacterial meningitis episode contracted recurrent meningitis. Neisseria meningitidis was the leading pathogen for first meningitis episodes (52%) and Streptococcus pneumoniae for recurrent episodes (52%). The median annual incidence of first episodes per 100 000 children decreased from 11.81 (IQR 11.26–17.60) in 1988–2003 to 2.60 (IQR 2.37–4.07) in 2004–2019 (IRR 0.25, 95% CI 0.23 to 0.26). The incidence of recurrences did not change: 0.06 (IQR 0.02–0.11) in 1988–2003 to 0.03 (IQR 0.00–0.06) in 2004–2019 (IRR 0.65, 95% CI 0.39 to 1.1). Age above 5 years (OR 3.6 (95% CI 1.5 to 8.3)) and a first episode due to Escherichia coli (OR 25.7 (95% CI 7.2 to 92.0)) were associated with higher risks of recurrence.Conclusion The recurrence rate of childhood bacterial meningitis in the Netherlands was 0.6%. While the incidence rate of first episodes decreased substantially, this was not the case for recurrent episodes. Older age and a first episode due to E. coli were associated with higher recurrence risks.

Abstract Generative deep learning models have emerged as a powerful approach for de novo drug design as they aid researchers in finding new molecules with desired properties. Despite continuous improvements in the field, a subset of the outputs that sequence-based de novo generators produce cannot be progressed due to errors. Here, we propose to fix these invalid outputs post hoc. In similar tasks, transformer models from the field of natural language processing have been shown to be very effective. Therefore, here this type of model was trained to translate invalid Simplified Molecular-Input Line-Entry System (SMILES) into valid representations. The performance of this SMILES corrector was evaluated on four representative methods of de novo generation: a recurrent neural network (RNN), a target-directed RNN, a generative adversarial network (GAN), and a variational autoencoder (VAE). This study has found that the percentage of invalid outputs from these specific generative models ranges between 4 and 89%, with different models having different error-type distributions. Post hoc correction of SMILES was shown to increase model validity. The SMILES corrector trained with one error per input alters 60–90% of invalid generator outputs and fixes 35–80% of them. However, a higher error detection and performance was obtained for transformer models trained with multiple errors per input. In this case, the best model was able to correct 60–95% of invalid generator outputs. Further analysis showed that these fixed molecules are comparable to the correct molecules from the de novo generators based on novelty and similarity. Additionally, the SMILES corrector can be used to expand the amount of interesting new molecules within the targeted chemical space. Introducing different errors into existing molecules yields novel analogs with a uniqueness of 39% and a novelty of approximately 20%. The results of this research demonstrate that SMILES correction is a viable post hoc extension and can enhance the search for better drug candidates. Graphical Abstract

Chestnut blight, caused by Cryphonectria parasitica, was identified in Devon, UK, in December 2016. Intensive surveys detected the disease at further sites in Devon (seven), Berkshire (one), Dorset (one), Derbyshire (four) and a cluster of eight sites in southeast London. Over 570 survey samples were tested, and 227 were positive for C. parasitica by isolation and real‐time PCR. A total of 227 isolates were tested for mating type, and 197 screened for vegetative compatibility group (VCG) and compared with VCGs known from mainland Europe. The same isolates were also screened for the presence of Cryphonectria hypovirus 1 (CHV‐1). Eleven VCGs were identified within the UK population. Five corresponded to already known European VCGs but six were unique. The European VCGs mainly came from the Devon, Dorset, Berkshire and Derbyshire disease outbreaks, whilst unique VCGs were almost exclusively from the southeast London cluster. Both mating types were detected, but only one mating type was present at each site, with the exception of a single Devon site. Perithecia of C. parasitica were never observed at any site. CHV‐1 was found in seven isolates from three different locations and was always subtype‐I, which has limited hypovirulence. Therefore, although CHV‐1 is associated with C. parasitica at some outbreaks, it probably has limited impact on virulence. The diversity of VCGs and their distribution at outbreak sites, together with findings of CHV‐1, suggests C. parasitica has been introduced to the UK multiple times over at least two decades through international plant trade. [ABSTRACT FROM AUTHOR]