Miracle-dz/UniFAR-FTUs huggingface dataset:
3D vascularised proximal tubules-on-a-multiplexed chip model for enhanced cell phenotypes

Modelling proximal tubule physiology and pharmacology is essential to understand tubular biology and guide drug discovery. To date, multiple models have been developed; however, their relevance to human disease has yet to be evaluated. Here, we report a 3D vascularized proximal tubule-on-a-multiplexed chip (3DvasPT-MC) device composed of co-localized cylindrical conduits lined with confluent epithelium and endothelium, embedded within a permeable matrix, and independently addressed by a closed-loop perfusion system. Each multiplexed chip contains six 3DvasPT models. We performed RNA-seq and compared the transcriptomic profile of proximal tubule epithelial cells (PTECs) and human glomerular endothelial cells (HGECs) seeded in our 3D vasPT-MCs and on 2D transwell controls with and without a gelatin–fibrin coating. Our results reveal that the transcriptional profile of PTECs is highly dependent on both the matrix and flow, while HGECs exhibit greater phenotypic plasticity and are affected by the matrix, PTECs, and flow. PTECs grown on non-coated Transwells display an enrichment of inflammatory markers, including TNF-a, IL-6, and CXCL6, resembling damaged tubules. However, this inflammatory response is not observed for 3D proximal tubules, which exhibit expression of kidney signature genes, including drug and solute transporters, akin to native tubular tissue. Likewise, the transcriptome of HGEC vessels resembled that of sc-RNAseq from glomerular endothelium when seeded on this matrix and subjected to flow. Our 3D vascularized tubule on chip model has utility for both renal physiology and pharmacology.
The Prognostic Effect of Immune Cell Infiltration Depends on Molecular Subtype in Endometrioid Ovarian Carcinomas

Abstract Purpose: Endometrioid ovarian carcinoma (ENOC) is the second most-common type of ovarian carcinoma, comprising 10%–20% of cases. Recently, the study of ENOC has benefitted from comparisons to endometrial carcinomas including defining ENOC with four prognostic molecular subtypes. Each subtype suggests differential mechanisms of progression, although tumor-initiating events remain elusive. There is evidence that the ovarian microenvironment may be critical to early lesion establishment and progression. However, while immune infiltrates have been well studied in high-grade serous ovarian carcinoma, studies in ENOC are limited. Experimental Design: We report on 210 ENOC, with clinical follow-up and molecular subtype annotation. Using multiplex IHC and immunofluorescence, we examine the prevalence of T-cell lineage, B-cell lineage, macrophages, and populations with programmed cell death protein 1 or programmed death-ligand 1 across subtypes of ENOC. Results: Immune cell infiltrates in tumor epithelium and stroma showed higher densities in ENOC subtypes with known high mutation burden (POLEmut and MMRd). While molecular subtypes were prognostically significant, immune infiltrates were not (overall survival P > 0.2). Analysis by molecular subtype revealed that immune cell density was prognostically significant in only the no specific molecular profile (NSMP) subtype, where immune infiltrates lacking B cells (TILB minus) had inferior outcome (disease-specific survival: HR, 4.0; 95% confidence interval, 1.1–14.7; P < 0.05). Similar to endometrial carcinomas, molecular subtype stratification was generally superior to immune response in predicting outcomes. Conclusions: Subtype stratification is critical for better understanding of ENOC, in particular the distribution and prognostic significance of immune cell infiltrates. The role of B cells in the immune response within NSMP tumors warrants further study.
Empirical Stochastic Models for the Dominant Climate Statistics of a General Circulation Model

Abstract Two Markov models with different dynamics and forcing are used to model the transient eddy statistics of an idealized general circulation model (GCM). The first Markov model employs a physically based dynamical operator composed of the linearized primitive equations plus spatially uniform damping. This model, when driven by spatially uncorrelated forcing, failed to produce reasonable fluxes and variances, despite tuning of the damping and forcing coefficients. This result contrasts with previous studies that have used the linearized quasigeostrophic equations to model extratropical eddy statistics. The second Markov model is constructed empirically from the time-lagged covariances of the GCM time series. This empirical Markov model could reproduce the dominant covariances over a range of time lags, provided it contained a sufficiently large number of degrees of freedom. It could not, however, reproduce the time-lag evolution of the trailing EOFs contained in the model. The errors in the trailing .
Group B Streptococcus CRISPR1 Typing of Maternal, Fetal, and Neonatal Infectious Disease Isolates Highlights the Importance of CC1 in In Utero Fetal Death

Group B Streptococcus is the leading bacterium responsible for maternal and neonatal infections worldwide, also involved in preterm birth, stillbirth, and fetal death. In this study, we determined the clonal complex of all GBS isolates responsible for neonatal diseases (early- and late-onset diseases) and maternal invasive infections, including chorioamnionitis associated with in utero fetal death. ABSTRACT We performed a descriptive analysis of group B Streptococcus (GBS) isolates responsible for maternal and fetal infectious diseases from 2004 to 2020 at the University Hospital of Tours, France. This represents 115 isolates, including 35 isolates responsible for early-onset disease (EOD), 48 isolates responsible for late-onset disease (LOD), and 32 isolates from maternal infections. Among the 32 isolates associated with maternal infection, 9 were isolated in the context of chorioamnionitis associated with in utero fetal death. Analysis of neonatal infection distribution over time highlighted the decrease in EOD since the early 2000s, while LOD incidence has remained relatively stable. All GBS isolates were analyzed by sequencing their CRISPR1 locus, which is an efficient way to determine the phylogenetic affiliation of strains, as it correlates with the lineages defined by multilocus sequence typing (MLST). Thus, the CRISPR1 typing method allowed us to assign a clonal complex (CC) to all isolates; among these isolates, CC17 was predominant (60/115, 52%), and the other main CCs, such as CC1 (19/115, 17%), CC10 (9/115, 8%), CC19 (8/115, 7%), and CC23 (15/115, 13%), were also identified. As expected, CC17 isolates (39/48, 81.3%) represented the majority of LOD isolates. Unexpectedly, we found mainly CC1 isolates (6/9) and no CC17 isolates that were responsible for in utero fetal death. Such a result highlights the possibility of a particular role of this CC in in utero infection, and further investigations should be conducted on a larger group of GBS isolated in a context of in utero fetal death. IMPORTANCE Group B Streptococcus is the leading bacterium responsible for maternal and neonatal infections worldwide, also involved in preterm birth, stillbirth, and fetal death. In this study, we determined the clonal complex of all GBS isolates responsible for neonatal diseases (early- and late-onset diseases) and maternal invasive infections, including chorioamnionitis associated with in utero fetal death. All GBS was isolated at the University Hospital of Tours from 2004 to 2020. We described the local group B Streptococcus epidemiology, which confirmed national and international data concerning neonatal disease incidence and clonal complex distribution. Indeed, neonatal diseases are mainly characterized by CC17 isolates, especially in late-onset disease. Interestingly, we identified mainly CC1 isolates responsible for in utero fetal death. CC1 could have a particular role in this context, and such a result should be confirmed on a larger group of GBS isolated from in utero fetal death.
High-volume patient-controlled epidural vs. programmed intermittent epidural bolus for labour analgesia: a randomised controlled study.

The aim of neuraxial analgesia is to achieve excellent pain relief with the fewest adverse effects. The most recently introduced technique for epidural analgesia maintenance is the programmed intermittent epidural bolus. In a recent study, we compared this with patient-controlled epidural analgesia without a background infusion and found that a programmed intermittent epidural bolus was associated with less breakthrough pain, lower pain scores, higher local anaesthetic consumption and comparable motor block. However, we had compared 10 ml programmed intermittent epidural boluses with 5 ml patient-controlled epidural analgesia boluses. To overcome this potential limitation, we designed a randomised, multicentre non-inferiority trial using 10 ml boluses in each group. The primary outcome was the incidence of breakthrough pain and total analgesic intake. Secondary outcomes included motor block; pain scores; patient satisfaction; and obstetric and neonatal outcomes. The trial was considered positive if two endpoints were met: non-inferiority of patient-controlled epidural analgesia with respect to breakthrough pain; and superiority of patient-controlled epidural analgesia with respect to local anaesthetic consumption. A total of 360 nulliparous women were allocated randomly to patient-controlled epidural analgesia-only or programmed intermittent epidural bolus groups. The patient-controlled group received 10 ml boluses of ropivacaine 0.12% with sufentanil 0.75 μg. ml-1 ; the programmed intermittent group received 10 ml boluses supplemented by 5 ml patient-controlled boluses. The lockout period was 30 min in each group and the maximum allowed hourly local anaesthetic/opioid consumption was identical between the groups. Breakthrough pain was similar between groups (11.2% patient controlled vs. 10.8% programmed intermittent, p = 0.003 for non-inferiority). Total ropivacaine consumption was lower in the PCEA-group (mean difference 15.3 mg, p < 0.001). Motor block, patient satisfaction scores and maternal and neonatal outcomes were similar across both groups. In conclusion, patient-controlled epidural analgesia is non-inferior to programmed intermittent epidural bolus if equal volumes of patient-controlled epidural analgesia are used to maintain labour analgesia and superior with respect to local anaesthetic consumption.
Clec12a inhibits MSU-induced immune activation through lipid raft expulsion

C type lection receptor Clec12a inhibits MSU induced Dendritic cell activation via its transmembrane domain rather than intracellular ITIM. Monosodium uric acid (MSU) crystal, the etiological agent of gout, has been shown to trigger innate immune responses via multiple pathways. It is known that MSU-induced lipid sorting on plasma membrane promotes the phosphorylation of Syk and eventually leads to the activation of phagocytes. However, whether this membrane lipid-centric mechanism is regulated by other processes is unclear. Previous studies showed that Clec12a, a member of the C-type lectin receptor family, is reported to recognize MSU and suppresses this crystalline structure-induced immune activation. How this scenario is integrated into the lipid sorting-mediated inflammatory responses by MSU, and particularly, how Clec12a intercepts lipid raft-originated signaling cascade remains to be elucidated. Here, we found that the ITIM motif of Clec12a is dispensable for its inhibition of MSU-mediated signaling; instead, the transmembrane domain of Clec12a disrupts MSU-induced lipid raft recruitment and thus attenuates downstream signals. Single amino acid mutagenesis study showed the critical role of phenylalanine in the transmembrane region for the interactions between C-type lectin receptors and lipid rafts, which is critical for the regulation of MSU-mediated lipid sorting and phagocyte activation. Overall, our study provides new insights for the molecular mechanisms of solid particle-induced immune activation and may lead to new strategies in inflammation control.
Factors associated with self-care behaviours among people with hypertension residing in Kathmandu: a cross-sectional study

Objective To determine the prevalence and associated factors of self-care behaviours among people with hypertension in the Kathmandu district of Nepal. Design Cross-sectional study. Setting Municipalities of Kathmandu district, Nepal. Participants We enrolled 375 adults aged ≥18 years with a minimum 1-year duration of hypertension using multistage sampling. Outcome measures We used the Hypertension Self-care Activity Level Effects to assess self-care behaviours and collected data through face-to-face interviews. We conducted univariate and multivariable logistic regression analyses to determine the factors associated with self-care behaviours. The results were summarised as crude and adjusted ORs (AORs) with 95% CIs. Results The adherence to antihypertensive medication, Dietary Approach to Stop Hypertension (DASH) diet, physical activity, weight management, alcohol moderation, and non-smoking were 61.3%, 9.3%, 59.2%, 14.1%, 90.9%, and 72.8%, respectively. Secondary or higher education (AOR: 4.42, 95% CI: 1.11 to 17.62), Brahmin and Chhetri ethnic groups (AOR: 3.30, 95% CI: 1.26 to 8.59) and good to very good perceived health (AOR: 3.96, 95% CI: 1.60 to 9.79) were positively associated with DASH diet adherence. Males (AOR: 2.05, 95% CI: 1.19 to 3.55) had higher odds of physical activity. Brahmin and Chhetri ethnic groups (AOR: 3.44, 95% CI: 1.63 to 7.26) and secondary or higher education (AOR: 4.70, 95% CI: 1.62 to 13.63) were correlates of weight management. Secondary or higher education (AOR: 2.47, 95% CI: 1.16 to 5.29), body mass index ≥25 kg/m2 (AOR: 1.83, 95% CI: 1.04 to 3.22) and income above the poverty line (AOR: 2.24, 95% CI: 1.08 to 4.63) were positively associated with non-smoking. Furthermore, Brahmin and Chhetri ethnic groups (AOR: 4.51, 95% CI: 1.64 to 12.40), males (AOR: 0.17, 95% CI: 0.06 to 0.50) and primary education (AOR: 0.26, 95% CI: 0.08 to 0.85) were associated with alcohol moderation. Conclusion The adherence to the DASH diet and weight management was particularly low. Healthcare providers and policymakers should focus on improving self-care by designing simple and affordable interventions for all patients with hypertension.
Effectiveness and Safety of Lacosamide, A Third-generation Anti-seizure Medication, for Poststroke Seizure and Epilepsy: A Literature Review

Advances in stroke treatment have resulted in a dramatic reduction in stroke mortality. Nevertheless, poststroke seizures and epilepsy are issues of clinical importance affecting survivors. Additionally, stroke is the most common cause of epilepsy in older adults. Although numerous antiseizure medications exist, studies are needed to provide robust evidence of the efficacy and tolerability of these medicines for treating poststroke seizures and epilepsy. Crucially, the newer generations of antiseizure medications require testing. Lacosamide, a third-generation antiseizure medication approved for treating localization-related epilepsy, has a novel mechanism of selectively enhancing the slow inactivation of sodium channels. This literature review evaluated whether lacosamide is effective and safe for the treatment of poststroke seizures and epilepsy. This review critically analyzed studies published in major academic databases (Pubmed, Embase, and Cochrane Library) from inception through June 2022 regarding the interaction of lacosamide with poststroke seizures and epilepsy. We included clinical prospective, retrospective, and case studies on patients with poststroke seizure and epilepsy, lacosamide as a treatment for seizures, neuroprotection in animal models of seizures, and the safety of lacosamide when coadministering anticoagulants. Clinical studies revealed lacosamide to be an effective antiseizure medication with high efficacy and tolerability in patients with poststroke seizures and epilepsy. In animal models, lacosamide proved effective at seizure reduction and neuroprotection. Pharmacokinetic studies demonstrated the safety of lacosamide when coadministering conventional and new anticoagulants. The literature suggests that Lacosamide is a promising candidate antiseizure medication for patients with poststroke seizures and epilepsy.
An adenoviral-vectored vaccine confers seroprotection against capsular group B meningococcal disease

Adenoviral-vectored vaccines are licensed for prevention of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Ebola virus, but, for bacterial proteins, expression in a eukaryotic cell may affect the antigen’s localization and conformation or lead to unwanted glycosylation. Here, we investigated the potential use of an adenoviral-vectored vaccine platform for capsular group B meningococcus (MenB). Vector-based candidate vaccines expressing MenB antigen factor H binding protein (fHbp) were generated, and immunogenicity was assessed in mouse models, including the functional antibody response by serum bactericidal assay (SBA) using human complement. All adenovirus-based vaccine candidates induced high antigen-specific antibody and T cell responses. A single dose induced functional serum bactericidal responses with titers superior or equal to those induced by two doses of protein-based comparators, as well as longer persistence and a similar breadth. The fHbp transgene was further optimized for human use by incorporating a mutation abrogating binding to the human complement inhibitor factor H. The resulting vaccine candidate induced high and persistent SBA responses in transgenic mice expressing human factor H. The optimized transgene was inserted into the clinically relevant ChAdOx1 backbone, and this vaccine has now progressed to clinical development. The results of this preclinical vaccine development study underline the potential of vaccines based on genetic material to induce functional antibody responses against bacterial outer membrane proteins. Description A single dose of a clinically-relevant adenovirus-based vaccine induces a strong and functional antibody response against group B meningococcus. Editor’s summary Adenoviral-vectored vaccines have been approved for viral infections, but their utility for protecting against bacterial infections is unclear. Here, Dold et al. evaluated an adenoviral-vectored vaccine platform for capsular group B meningococcus. The authors found, using multiple mouse models, that the vaccine candidates elicited robust antibody and T cell responses with a single dose. The authors further optimized the vaccine and incorporated the immunogen into the ChAdOx1 backbone. This vaccine, which is now in clinical development, may offer a single shot option for meningococcal disease. —Courtney Malo
Beyond Learned Metadata-based Raw Image Reconstruction

While raw images have distinct advantages over sRGB images, e.g., linearity and fine-grained quantization levels, they are not widely adopted by general users due to their substantial storage requirements. Very recent studies propose to compress raw images by designing sampling masks within the pixel space of the raw image. However, these approaches often leave space for pursuing more effective image representations and compact metadata. In this work, we propose a novel framework that learns a compact representation in the latent space, serving as metadata, in an end-to-end manner. Compared with lossy image compression, we analyze the intrinsic difference of the raw image reconstruction task caused by rich information from the sRGB image. Based on the analysis, a novel backbone design with asymmetric and hybrid spatial feature resolutions is proposed, which significantly improves the rate-distortion performance. Besides, we propose a novel design of the context model, which can better predict the order masks of encoding/decoding based on both the sRGB image and the masks of already processed features. Benefited from the better modeling of the correlation between order masks, the already processed information can be better utilized. Moreover, a novel sRGB-guided adaptive quantization precision strategy, which dynamically assigns varying levels of quantization precision to different regions, further enhances the representation ability of the model. Finally, based on the iterative properties of the proposed context model, we propose a novel strategy to achieve variable bit rates using a single model. This strategy allows for the continuous convergence of a wide range of bit rates. Extensive experimental results demonstrate that the proposed method can achieve better reconstruction quality with a smaller metadata size.
Investigating Pre-trained Language Models on Cross-Domain Datasets, a Step Closer to General AI

Pre-trained language models have recently emerged as a powerful tool for fine-tuning a variety of language tasks. Ideally, when models are pre-trained on large amount of data, they are expected to gain implicit knowledge. In this paper, we investigate the ability of pre-trained language models to generalize to different non-language tasks. In particular, we test them on tasks from different domains such as computer vision, reasoning on hierarchical data, and protein fold prediction. The four pre-trained models that we used, T5, BART, BERT, and GPT-2 achieve outstanding results. They all have similar performance and they outperform transformers that are trained from scratch by a large margin. For instance, pre-trained language models perform better on the Listops dataset, with an average accuracy of 58.7\%, compared to transformers trained from scratch, which have an average accuracy of 29.0\%. The significant improvement demonstrated across three types of datasets suggests that pre-training on language helps the models to acquire general knowledge, bringing us a step closer to general AI. We also showed that reducing the number of parameters in pre-trained language models does not have a great impact as the performance drops slightly when using T5-Small instead of T5-Base. In fact, when using only 2\% of the parameters, we achieved a great improvement compared to training from scratch. Finally, in contrast to prior work, we find out that using pre-trained embeddings for the input layer is necessary to achieve the desired results.
LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models

Large foundation models have demonstrated a great ability to achieve general human-level intelligence far beyond traditional approaches. As the technique keeps attracting attention from the AI community, more and more large foundation models have become publically available. However, most of those models exhibit a major deficiency in specialized-task applications, where the step of finetuning is still required for obtaining satisfactory performance. As the number of available models and specialized tasks keeps growing, the job of general finetuning becomes highly nontrivial. In this paper, we take the first step to address this issue. We introduce an extensible and lightweight toolkit, LMFlow, which aims to simplify the finetuning and inference of general large foundation models. LMFlow offers a complete finetuning workflow for a large foundation model to support personalized training with limited computing resources. Furthermore, it supports continuous pretraining, instruction tuning, parameter-efficient finetuning, alignment tuning, and large model inference, along with carefully designed and extensible APIs. This toolkit has been thoroughly tested and is available at https://github.com/OptimalScale/LMFlow.