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Analyzing heterogeneous treatment effects (HTEs) plays a crucial role in understanding the impacts of educational interventions. A standard practice for HTE analysis is to examine interactions between treatment status and preintervention participant characteristics, such as pretest scores, to identify how different groups respond to treatment. This study demonstrates that the identical patterns of HTE on test score outcomes can emerge either from variation in treatment effects due to a preintervention participant characteristic or from correlations between treatment effects and item easiness parameters. We demonstrate analytically and through simulation that these two scenarios cannot be distinguished if analysis is based on summary scores alone. We then describe a novel approach that identifies the relevant data-generating process by leveraging item-level data. We apply our approach to a randomized trial of a reading intervention in second grade and show that any apparent HTE by pretest ability is driven by the correlation between treatment effect size and item easiness. Our results highlight the potential of employing measurement principles in causal analysis, beyond their common use in test construction.

The study inspects the status of school governance and school outcome at primary school level and set up roadmap for all the stakeholders to achieve the mandate of Goal 4 in SDGs within 2030, especially in the context of rural India. The status of school governance and school outcome are assessed under 4 dimensions and 16 parameters through a survey of 21 rural primary schools from rural Maharashtra. Two different indices have been constructed for school outcomes and school governance using multi-stage principal component analysis. Public and private-aided schools are compared according to the degree of accountability and transparency. It has been realized that there is an absolute need for strong school governance at ground level which is very poor across public schools in rural India. The study followed the Worldwide Governance Indicators (WGI) project and ASER (2014) and considered these baselines to find the present status of school governance and school outcome for the present study.

Bile acids (BA) are among the most abundant metabolites produced by the gut microbiome. Primary BAs produced in the liver are converted by gut bacterial 7-α-dehydroxylation into secondary BAs, which can differentially regulate host health via signaling based on their varying affinity for BA receptors. Despite the importance of secondary BAs in host health, the regulation of 7-α-dehydroxylation and the role of diet in modulating this process is incompletely defined. Understanding this process could lead to dietary guidelines that beneficially shift BA metabolism. Dietary fiber regulates gut microbial composition and metabolite production. We tested the hypothesis that feeding mice a diet rich in a fermentable dietary fiber, resistant starch (RS), would alter gut bacterial BA metabolism. Male and female wild-type mice were fed a diet supplemented with RS or an isocaloric control diet (IC). Metabolic parameters were similar between groups. RS supplementation increased gut luminal deoxycholic acid (DCA) abundance. However, gut luminal cholic acid (CA) abundance, the substrate for 7-α-dehydroxylation in DCA production, was unaltered by RS. Further, RS supplementation did not change the mRNA expression of hepatic BA producing enzymes or ileal BA transporters. Metagenomic assessment of gut bacterial composition revealed no change in the relative abundance of bacteria known to perform 7-α-dehydroxylation. P. ginsenosidimutans and P. multiformis were positively correlated with gut luminal DCA abundance and increased in response to RS supplementation. These data demonstrate that RS supplementation enriches gut luminal DCA abundance without increasing the relative abundance of bacteria known to perform 7-α-dehydroxylation.

RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA three-dimensional structure prediction. With agreement from structural biologists, RNA structures are predicted by modeling groups before publication of the experimental structures. We report a large-scale set of predictions by 18 groups for 23 RNA-Puzzles: 4 RNA elements, 2 Aptamers, 4 Viral elements, 5 Ribozymes and 8 Riboswitches. We describe automatic assessment protocols for comparisons between prediction and experiment. Our analyses reveal some critical steps to be overcome to achieve good accuracy in modeling RNA structures: identification of helix-forming pairs and of non-Watson-Crick modules, correct coaxial stacking between helices and avoidance of entanglements. Three of the top four modeling groups in this round also ranked among the top four in the CASP15 contest.

Plasma cell-rich rejection is a rare and poorly defined entity. Its treatment is not clearly defined and has universally poor prognosis. More data should be published from various transplant centers around the world to identify the treatment that has the best outcomes and to formulate treatment guidelines for these cases. It is a retrospective analysis of kidney biopsies form 2008 to 2018. Four hundred biopsied were screened and 55 were found to have features of rejection and among them, 13 had plasma cell-rich rejection. Data of treatment given and the graft survival outcomes in these cases were retrieved by medical records. One patient had complete recovery, three had graft loss and the remaining nine had permanent decline in glomerular filtration rate. Decrease in immunosuppression and presence of infection are risk factors for plasma cell-rich acute rejection (PCAR). It can be acute cell-mediated rejection (ACR)/antibody-mediated rejection (AMR)/ACR+AMR. Resistant rejection, ACR+AMR, C4d positivity, and severe interstitial inflammation are poor prognostic factors. Overzealous decrease in immunosuppression should not be done. Management of immunosuppression during infection is most critical for the development of PCAR. Bortezomib is emerging as a therapeutic modality for the treatment of PCAR.

This study aims to explore university students' perception regarding an e-assessment system in terms of preference, assessment management, and the quality assurance process. A cross-sectional online survey was employed among 384 students of Nepal Open University (NOU). Structural equation modeling (SEM) was used in the research. Findings indicate that the perception of students regarding preference, e-assessment management, and the quality assurance process was found to be significantly high. In particular, the perception of preference regarding digital assessment was higher than assessment management and the quality assurance process. Gender, ethnicity, and place for taking classes, have an effect on implementation. This study concludes that three attributions of preference for digital assessment, management, and quality assurance are affected by one another.

Efficient image annotation is necessary to utilize deep learning object recognition neural networks in nuclear safeguards, such as for the detection and localization of target objects like nuclear material containers (NMCs). This capability can help automate the inventory accounting of different types of NMCs within nuclear storage facilities. The conventional manual annotation process is labor-intensive and time-consuming, hindering the rapid deployment of deep learning models for NMC identifications. This paper introduces a novel semi-automatic method for annotating 2D images of nuclear material containers (NMCs) by combining 3D light detection and ranging (LiDAR) data with color and depth camera images collected from a handheld scan system. The annotation pipeline involves an operator manually marking new target objects on a LiDAR-generated map, and projecting these 3D locations to images, thereby automatically creating annotations from the projections. The semi-automatic approach significantly reduces manual efforts and the expertise in image annotation that is required to perform the task, allowing deep learning models to be trained on-site within a few hours. The paper compares the performance of models trained on datasets annotated through various methods, including semi-automatic, manual, and commercial annotation services. The evaluation demonstrates that the semi-automatic annotation method achieves comparable or superior results, with a mean average precision (mAP) above 0.9, showcasing its efficiency in training object recognition models. Additionally, the paper explores the application of the proposed method to instance segmentation, achieving promising results in detecting multiple types of NMCs in various formations.

ChatGPT has emerged as a significant subject of research and exploration, casting a critical spotlight on teaching and learning practices in the higher education domain. This study examines the most influential articles, leading journals, and productive countries concerning citations and publications related to ChatGPT in higher education, while also shedding light on emerging thematic and geographic clusters within research on ChatGPT's role and challenges in teaching and learning at higher education institutions. Forty-seven research papers from the Scopus database were shortlisted for bibliometric analysis. The findings indicate that the use of ChatGPT in higher education, particularly issues of academic integrity and research, has been studied extensively by scholars in the United States, who have produced the largest volume of publications, alongside the highest number of citations. This study uncovers four distinct thematic clusters (academic integrity, learning environment, student engagement, and scholarly research) and highlights the predominant areas of focus in research related to ChatGPT in higher education, including student examinations, academic integrity, student learning, and field-specific research, through a country-based bibliographic analysis. Plagiarism is a significant concern in the use of ChatGPT, which may reduce students' ability to produce imaginative, inventive, and original material. This study offers valuable insights into the current state of ChatGPT in higher education literature, providing essential guidance for scholars, researchers, and policymakers.

Background: Rapid automatised naming (RAN) has been found to predict children's reading and arithmetic abilities. However, the underlying mechanisms for its involvement in the two abilities are not clear. This study examines how RAN shared variances with domain-general and domain-specific abilities in predicting reading and arithmetic in Chinese children. Methods: One hundred and sixty-four children (mean age = 8 years 0 months, SD = 4 months) were administered with RAN tasks, word reading and arithmetic tasks and measures of working memory, processing speed, morphological awareness, phonological awareness and number line estimation. Results: RAN mainly shared variance with morphological awareness in predicting word reading, while it shared variance with processing speed and number line estimation in predicting arithmetic calculation. Conclusions: The findings indicated that RAN was related to reading and arithmetic for different reasons. The RAN-reading relationship partly reflected the semantic facilitation of the orthography-phonology links for both RAN stimuli and Chinese characters, while the RAN-arithmetic relationship partly reflected the shared process of retrieving semantic information from long-term memory embedded in the two tasks.

The earthquakes which struck Nepal's capital in 2015 were humanitarian disasters. Not only did they inflict tragic loss of life and livelihoods, they also destroyed parts of the Kathmandu Valley's unique UNESCO World Heritage site. These monuments were not just ornate structures but living monuments playing central roles in the daily lives of thousands, representing portals where the heavens touch earth and people commune with guiding deities. Their rehabilitation was also of economic importance as they represent a major source of tourist income and employment. Unfortunately, the social and political desire for rapid reconstruction resulted in the swift removal of many traditionally constructed foundations and their replacement with modern materials without assessments of whether they contributed towards the collapse of individual monuments. These actions, combined with the wholesale removal, mixing and dumping of modern and historic debris, contributed to a second, equally destructive, cultural catastrophe -- irreversible damage to Kathmandu's Medieval fabric, in a process which frequently excluded local communities and custodians. This case study draws from our collective reflections and lessons learned from our attempts to enable equitable and ethical research partnerships between UK and Nepali colleagues as well as local communities in the debris of the Kasthamandap, Kathmandu's eponymous monument. After briefly describing the potential of mobilising archaeologists in post-disaster contexts and outlining the challenges of undertaking research in such a setting, our case study utilises the TRUST Code to assess the character and success of our multidisciplinary collaboration in a time of crises.

Type 2 diabetes mellitus (T2DM) is a progressive disease with multifactorial etiology. The first-line therapy includes monotherapy (with metformin), which often fails to provide effective glycemic control, necessitating the addition of add-on therapy. In this regard, multiple single-dose agents formulated as a single-dose form called fixed-dose combinations (FDCs) have been evaluated for their safety, efficacy, and tolerability. The primary objective of this review is to develop practice-based expert group opinion on the current status and the causes of concern regarding the irrational use of FDCs, in Indian settings. After due discussions, the expert group analyzed the results from several clinical evidence in which various fixed combinations were used in T2DM management. The panel opined that FDCs (double or triple) improve patient adherence, reduce cost, and provide effective glycemic control and, thereby, play an important role in the management of T2DM. The expert group strongly recommended that the irrational metformin FDC's, banned by Indian government, should be stopped and could be achieved through active participation from the government, regulatory bodies, and health ministry, and through continuous education of primary care physicians and pharmacists. In T2DM management, FDCs play a crucial role in achieving glycemic targets effectively. However, understanding the difference between rational and irrational FDC combinations is necessary from the safety, efficacy, and tolerability perspective. In this regard, primary care physicians will have to use a multistep approach so that they can take informed decisions.

The design and use of synthetic communities, or SynComs, is one of the most promising strategies for disentangling the complex interactions within microbial communities, and between these communities and their hosts. Compared to natural communities, these simplified consortia provide the opportunity to study ecological interactions at tractable scales, as well as facilitating reproducibility and fostering interdisciplinary science. However, the effective implementation of the SynCom approach requires several important considerations regarding the development and application of these model systems. There are also emerging ethical considerations when both designing and deploying SynComs in clinical, agricultural or environmental settings. Here we outline current best practices in developing, implementing and evaluating SynComs across different systems, including a focus on important ethical considerations for SynCom research.

Hospital quality measures are a vital component of a learning health system, yet they can be costly to report, statistically underpowered, and inconsistent due to poor interrater reliability. Large language models (LLMs) have recently demonstrated impressive performance on health care-related tasks and offer a promising way to provide accurate abstraction of complete charts at scale. To evaluate this approach, we deployed an LLM-based system that ingests Fast Healthcare Interoperability Resources data and outputs a completed Severe Sepsis and Septic Shock Management Bundle (SEP-1) abstraction. We tested the system on a sample of 100 manual SEP-1 abstractions that University of California San Diego Health reported to the Centers for Medicare & Medicaid Services in 2022. The LLM system achieved agreement with manual abstractors on the measure category assignment in 90 of the abstractions (90%; κ=0.82; 95% confidence interval, 0.71 to 0.92). Expert review of the 10 discordant cases identified four that were mistakes introduced by manual abstraction. This pilot study suggests that LLMs using interoperable electronic health record data may perform accurate abstractions for complex quality measures. (Funded by the National Institute of Allergy and Infectious Diseases [1R42AI177108-1] and others.).

The intricate network of protein-chaperone interactions is crucial for maintaining cellular function. Recent discoveries have unveiled the existence of specialized chaperone assemblies, known as epichaperomes, which serve as scaffolding platforms that orchestrate the reconfiguration of protein-protein interaction networks, thereby enhancing cellular adaptability and proliferation. This study explores the structural and regulatory aspects of epichaperomes, with a particular focus on the role of post-translational modifications (PTMs) in their formation and function. A key finding is the identification of specific PTMs on HSP90, particularly at residues Ser226 and Ser255 within an intrinsically disordered region, as critical determinants of epichaperome assembly. Our data demonstrate that phosphorylation of these serine residues enhances HSP90s interactions with other chaperones and co-chaperones, creating a microenvironment conducive to epichaperome formation. Moreover, we establish a direct link between epichaperome function and cellular physiology, particularly in contexts where robust proliferation and adaptive behavior are essential, such as in cancer and pluripotent stem cell maintenance. These findings not only provide mechanistic insights but also hold promise for the development of novel therapeutic strategies targeting chaperone assemblies in diseases characterized by epichaperome dysregulation, thereby bridging the gap between fundamental research and precision medicine.

PurposeThis study assessed the safety and efficacy of transepithelial crosslinking (CXL) using femtosecond (FS) laser-machined epithelial microchannels (MCs) followed by UVA CXL compared to FS laser (NLO CXL) in rabbits.MethodsThe epithelium of 36 rabbits was machined to create 2- by 25-µm MCs at 400 MCs/mm2. Eyes were treated with 1% riboflavin (Rf) solution for 30 minutes, rinsed, and then crosslinked using UVA or NLO CXL. Rabbits were monitored by epithelial staining, optical coherence tomography (OCT) imaging, and esthesiometry. After sacrifice at 2, 4, or 8 weeks, corneas were examined for collagen autofluorescence and immunohistochemistry.ResultsNLO CXL showed no epithelial damage compared to UVA CXL, which produced on average 23.89 ± 5.6 mm2 epithelial defects that healed by day 3. UVA CXL also produced loss of corneal sensitivity averaging 0.83 ± 0.24 cm force to elicit a blink response that persisted for 28 days and remained significantly lower than control or NLO CXL. OCT imaging detected the presence of a demarcation line only following UVA CXL but not NLO CXL.ConclusionsEven with improved transepithelial Rf penetration, UVA CXL resulted in severe epithelial damage, loss of corneal sensitivity, and delayed wound healing persisting for a month. When MCs were paired with NLO CXL, however, these issues were mostly negated. This suggests that MC NLO CXL can achieve a faster visual recovery without postoperative pain or risk of infection.Translational relevanceUVA CXL is a successful procedure, but there is a need for a transepithelial protocol. The combination of MCs and NLO CXL is able to keep the benefits of UVA CXL without causing epithelial damage.

BACKGROUND: Point-of-care electroencephalography (EEG) devices can be rapidly applied and do not require specialized technologists, creating new opportunities to use EEG during prehospital care. We evaluated the feasibility of point-of-care EEG during ambulance transport for 911 calls. METHODS: This mixed-methods study was conducted between May 28, 2022 and October 28, 2023. Emergency Medical Services (EMS) clinicians identified eligible individuals, provided emergent treatment, applied EEG, and obtained an EEG recording during ambulance transport. Eligible patients were aged 6 years or older and evaluated for seizure, stroke, or altered mental status. EMS clinicians completed a survey and a brief phone interview following every enrollment. Two epileptologists reviewed EEG recordings for interpretability and artifact. RESULTS: There were 34 prehospital encounters in which EEG was applied. Patients had a mean age of 69 years, and 15 (44%) were female. EEG recordings had a median duration of 10 min 30 s. It took EMS clinicians an average of 2.5 min to apply the device and begin EEG recording. There were 14 (47%) recordings where clinicians achieved a high-quality connection for all 10 electrodes and 32 (94%) recordings that were sufficient in quality to interpret. There were 24 (71%) recordings with six or more channels free of artifact for 5 min or more. All clinicians agreed or strongly agreed that the device was easy to use. CONCLUSION: Among real-world prehospital encounters for patients with neurologic symptoms, point-of-care EEG was rapidly applied and yielded EEG recordings that could be used for clinical interpretation, demonstrating the feasibility of point-of-care EEG in future prehospital care.

Large-scale sequencing efforts have been undertaken to understand the mutational landscape of the coding genome. However, the vast majority of variants occur within non-coding genomic regions. We designed an integrative computational and experimental framework to identify recurrently mutated non-coding regulatory regions that drive tumor progression. Applying this framework to sequencing data from a large prostate cancer patient cohort revealed a large set of candidate drivers. We used (1) in silico analyses, (2) massively parallel reporter assays, and (3) in vivo CRISPR interference screens to systematically validate metastatic castration-resistant prostate cancer (mCRPC) drivers. One identified enhancer region, GH22I030351, acts on a bidirectional promoter to simultaneously modulate expression of the U2-associated splicing factor SF3A1 and chromosomal protein CCDC157. SF3A1 and CCDC157 promote tumor growth in vivo. We nominated a number of transcription factors, notably SOX6, to regulate expression of SF3A1 and CCDC157. Our integrative approach enables the systematic detection of non-coding regulatory regions that drive human cancers.

Metastatic castration-resistant prostate cancer (mCRPC) is a lethal disease that resists therapy targeting androgen signaling, the primary driver of prostate cancer. mCRPC resists androgen receptor (AR) inhibitors by amplifying AR signaling or by evolving into therapy-resistant subtypes that do not depend on AR. Elucidation of the epigenetic underpinnings of these subtypes could provide important insights into the drivers of therapy resistance. In this study, we produced chromatin accessibility maps linked to the binding of lineage-specific transcription factors (TF) by performing assay for transposase-accessible chromatin sequencing on 70 mCRPC tissue biopsies integrated with transcriptome and whole-genome sequencing. mCRPC had a distinct global chromatin accessibility profile linked to AR function. Analysis of TF occupancy across accessible chromatin revealed 203 TFs associated with mCRPC subtypes. Notably, ZNF263 was identified as a putative prostate cancer TF with a significant impact on gene activity in the double-negative subtype (AR- neuroendocrine-), potentially activating MYC targets. Overall, this analysis of chromatin accessibility in mCRPC provides valuable insights into epigenetic changes that occur during progression to mCRPC. Significance: Integration of a large cohort of transcriptome, whole-genome, and ATAC sequencing characterizes the chromatin accessibility changes in advanced prostate cancer and identifies therapy-resistant prostate cancer subtype-specific transcription factors that modulate oncogenic programs.

In our cells, a limited number of RNA binding proteins (RBPs) are responsible for all aspects of RNA metabolism across the entire transcriptome. To accomplish this, RBPs form regulatory units that act on specific target regulons. However, the landscape of RBP combinatorial interactions remains poorly explored. Here, we perform a systematic annotation of RBP combinatorial interactions via multimodal data integration. We build a large-scale map of RBP protein neighborhoods by generating in vivo proximity-dependent biotinylation datasets of 50 human RBPs. In parallel, we use CRISPR interference with single-cell readout to capture transcriptomic changes upon RBP knockdowns. By combining these physical and functional interaction readouts, along with the atlas of RBP mRNA targets from eCLIP assays, we generate an integrated map of functional RBP interactions. We then use this map to match RBPs to their context-specific functions and validate the predicted functions biochemically for four RBPs. This study provides a detailed map of RBP interactions and deconvolves them into distinct regulatory modules with annotated functions and target regulons. This multimodal and integrative framework provides a principled approach for studying post-transcriptional regulatory processes and enriches our understanding of their underlying mechanisms.

Schizophrenia and bipolar disorder are associated with cognitive deficits that contribute significantly to disability. However, traditional in-lab cognitive assessments are time-consuming and not optimized for remote administration. Recent advancements in smartphone technology enable momentary cognitive assessments in a real-world context. This brief report reviews recent research in momentary cognitive assessments in individuals with schizophrenia and bipolar disorder through reviewing mobile platforms and cognitive assessments studied. A total of 14 experimental articles were reviewed, focusing on cognitive domains including visual working memory, processing speed, executive function, verbal fluency, verbal memory, social cognition, and typing patterns. The review highlights the feasibility of remote cognitive assessment with smartphones, and provides a layout of domains studied in this context, but illustrates a low volume of current research, the need for additional studies, and the potential for innovations like digital phenotyping.

Author(s): Babu Lal [1]; Ragavi Alagarsamy [2]; Jitendra Kumar (corresponding author) [3]; Anshul J. Rai [3]; Vineeta Yadav [4]; Rajnish Joshi [5]; Md. Yunus [1] INTRODUCTION Postoperative nausea and vomiting [...], Background and Aims: Postoperative nausea and vomiting (PONV) is a common and distressing complication in all types of surgeries involving general anaesthesia. To establish evidence for best clinical practices, this meta-analysis compares the efficacy and safety of palonosetron and ondansetron in preventing PONV in patients undergoing non-laparoscopic surgeries. Methods: A PRISMA-guided systematic review and meta-analysis was conducted in PubMed, Google Scholar, Semantic Scholar, and Cochrane Library to identify comparative studies that reported the efficacy (nausea and vomiting) at postoperative time points, namely T1 (0-2 hours), T2 (0-6 hours), T3 (12-24 hours), T4 (24-48 hours), and T5 (24-72 hours), as well as safety (number of incidence of adverse effects). A meta-analysis of the efficacy and safety groups was performed using a random-effects model. Results: Nineteen randomised controlled trials were included. Pooled risk ratio (RR) revealed that patients receiving palonosetron were significantly less likely to develop nausea [0-2 h, RR = 0.82 (95% confidence interval (CI): 0.50, 1.34), P = 0.317, I[sup.2] = 15.3%], [0-6 h, RR = 0.76 (95% CI: 0.44, 1.29), P = 0.137, I[sup.2] = 45.7%], [12-24 h, RR = 0.39 (95%CI: 0.16, 0.96), P = 0.088, I[sup.2] = 54.2%], [24-48 h, RR = 0.44 (95% CI: 0.20, 0.96), P = 0.598, I[sup.2] = 0%], [24-72 h, RR 0.22 (95% CI: 0.08, 0.57), P = 0.119, I[sup.2] = 53.0%] and vomiting [0-2 h, RR = 0.59 (95% CI: 0.29, 1.23), P = 0.868, I[sup.2] = 0%], [0-6 h, RR = 1.42 (95% CI: 0.74, 2.72), P = 0.790, I[sup.2] = 0%], [12-24 h, RR = 0.14 (95% CI: 0.04, 0.51), P = 0.749, I[sup.2] = 0.0%], [24-48 h, RR = 0.24 (95%CI: 0.09, 0.62), P = 0.561, I[sup.2] = 0%], [24-72 h, RR = 0.11 (95% CI: 0.02, 0.58), P = 0.859, I[sup.2] = 0%]. The safety profiles of palonosetron and ondansetron were comparable [headache: RR = 0.82 (95%CI: 0.65, 1.04), P = 0.940, I[sup.2] = 0%], [drowsiness: RR = 0.96 (95%CI: 0.54, 1.71), P = 0.870, I[sup.2] = 0%], [constipation: RR=1.20 (95%CI: 0.52, 2.79), P = 0.650, I[sup.2] = 0%], [dizziness: RR = 0.60 (95%CI: 0.44, 0.83), P = 0.644, I[sup.2] = 0%]. Conclusion: Palonosetron and ondansetron exhibited comparable efficacy in the early hours (0-6 h). Palonosetron showed superior efficacy beyond 6 hours, providing sustained PONV prophylaxis in patients undergoing various surgeries, excluding laparoscopic procedures. Keywords: Antiemetic drugs, meta-analysis, nausea, ondansetron, palonosetron, postoperative nausea and vomiting, systematic review, vomiting