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Determining the structure and phenotypic context of molecules detected in untargeted metabolomics experiments remains challenging. Here we present reverse metabolomics as a discovery strategy, whereby tandem mass spectrometry spectra acquired from newly synthesized compounds are searched for in public metabolomics datasets to uncover phenotypic associations. To demonstrate the concept, we broadly synthesized and explored multiple classes of metabolites in humans, including N-acyl amides, fatty acid esters of hydroxy fatty acids, bile acid esters and conjugated bile acids. Using repository-scale analysis1,2, we discovered that some conjugated bile acids are associated with inflammatory bowel disease (IBD). Validation using four distinct human IBD cohorts showed that cholic acids conjugated to Glu, Ile/Leu, Phe, Thr, Trp or Tyr are increased in Crohn's disease. Several of these compounds and related structures affected pathways associated with IBD, such as interferon-γ production in CD4+ T cells3 and agonism of the pregnane X receptor4. Culture of bacteria belonging to the Bifidobacterium, Clostridium and Enterococcus genera produced these bile amidates. Because searching repositories with tandem mass spectrometry spectra has only recently become possible, this reverse metabolomics approach can now be used as a general strategy to discover other molecules from human and animal ecosystems.

Introduction: Dyslipidemia comprises disorders of lipoprotein metabolism, which is usually characterized by increased plasma levels of total cholesterol (TC), triglycerides (TG), low-density lipoproteins (LDL), or reduced plasma level of high-density lipoprotein (HDL). Dyslipidemia is commonly found in Type 2 Diabetes Mellitus (T2DM) patients causing an increased risk of atherosclerosis and cardiovascular disease. Many studies done previously show variable prevalence of dyslipidemia (68 to 85 %) in known diagnosed patients of T2DM (with unknown history of onset), however, only limited studies have been done for finding prevalence of dyslipidemia in newly detected T2DM patients. AIM: This study is a cross-sectional observational study done in a tertiary care hospital in Mumbai (India), aiming to study prevalence and pattern in newly detected T2DM patients. The secondary objective is to compare the prevalence and pattern of dyslipidemia in newly detected T2DM patients with non-diabetic controls. Material and Method: For this, 100 newly detected T2DM patients were chosen along with 100 cases of age & gender-matched non-diabetic controls. Their clinical profile and lipid profile of both groups were observed and compared. Result: Results showed higher prevalence of dyslipidemia in newly detected T2DM patients (64%) which was significantly more than the control group (32%). Moreover, the pattern of dyslipidemia showed greater mean levels of TC, TG, and LDL and low mean levels of HDL in T2DM patients compared to the control group. Conclusion: These findings show that there is an increased risk of Dyslipidemia in new onset T2DM patients.

Background: As the male factor in infertility is on rising trend, we need a promising tool for sperm selection to improve the embryo development. Aim: The present study compares the embryological and clinical parameters between physiological intracytoplasmic sperm injection (PICSI) and conventional intracytoplasmic sperm injection (ICSI) in abnormal semen parameters. Settings and Design: This was a retrospective cohort study. Materials and Methods: This study was conducted over the duration of 9 months from April to December 2023. One hundred and four patients, who matched the inclusion and exclusion criteria, were enrolled in the study. The adjusted blastulation rate was evaluated as the primary outcome, and the secondary outcomes studied were fertilisation rate, good-quality cleavage stage embryo rate, clinical pregnancy and miscarriage rate. Statistical Analysis Used: The outcome measures were calculated using independent Student’s t-test, and P < 0.5 was considered statistically significant. Results: The 104 enrolled patients were divided into two groups. In the study population (n = 51), PICSI was used for sperm selection and the controls (n = 53) used conventional ICSI. The adjusted blastulation rate was statistically significant higher in the PICSI group (43.7%) as compared to the controls (34.2%) (P = 0.022). The fertilisation and cleavage rates were not statistically different amongst the groups. The pregnancy rate (37.2% vs. 32%, P = 0.027) and miscarriage rate (5.3% vs. 11.7%, P = 0.005) were statistically better in the PICSI group. Conclusion: PICSI may improve treatment outcomes in couples undergoing assisted reproduction for male factor infertility. However, larger randomised controlled trials are needed for validating the current study findings.

Eosinophilia can be due to both infectious and non-infectious causes, many of which may be clinically indistinguishable. Filariasis, a tropical and subtropical infection, is caused by Wuchereria bancrofti, Brugia timori (B. timori), and Brugia malayi. Filariasis is conventionally diagnosed by demonstration of microfilaria in the peripheral blood smear. The disease may be missed if one is not aware of the possibility. We report two cases in two individuals with eosinophilia due to filaria resulting in tropical pulmonary eosinophilia and deep vein thrombosis (DVT). They had complete symptomatic improvement after a 3-week course of diethylcarbamazine (DEC) +/- ivermectin.

Abstract Proper utilization of agricultural land is a big challenge as they often laid over as waste lands. Farming is a significant occupation in any country and improving it further by promoting more farming opportunities will take the country towards making a huge leap forward. The issue in achieving this would be the lack of knowledge of cultivable land for food crops. The objective of this work is to utilize modern computer vision technology to identify and map cultivable land for agricultural needs. With increasing population and demand for food, improving the farming sector is crucial. However, the challenge lies in the lack of suitable land for food crops cultivation. To tackle this issue, we propose to use sophisticated image processing techniques on satellite images of the land to determine the regions that are capable of growing food crops. The solution architecture includes enhancement of satellite imagery using sophisticated pan sharpening techniques, notably the Brovey transformation, aiming to transform dull satellite images into sharper versions, thereby improving the overall quality and interpretability of the visual data. Making use of the weather data on the location observed and taking into factors like the soil moisture, weather, humidity, wind, sunlight times and so on, this data is fed into a generative pre-trained transformer model which makes use of it and gives a set of crops that are suitable to be grown on this piece of land under the said conditions. The results obtained by the proposed fusion approach is compared with the dataset provided by the government for different states in India and the performance was measured. We achieved an accuracy of 80% considering the crop suggested by our model and the predominant crop of the region. Also, the classification report detailing the performance of the proposed model is presented.

Organic and Perovskite solar cells have attracted a lot of attention recently since they can be used with flexible substrates and have lower manufacturing costs. The configuration and materials employed in their construction, including the Electron Transport Layer (ETL), active layer, electrode contact, and hole transport layer greatly influence the stability and performance of these solar cells. This research focuses on the simulation of solar cells, specifically utilizing zinc oxide (ZnO) as the electron transport layer. A 0.1 molar ZnO thin film was prepared from Zinc acetate salt and was deposited on a glass substrate using the cost effective Successive Ionic Layer Adsorption and Reaction (SILAR) method. In-depth investigations were carried out on several factors, including structural, surface, optical and numerical analysis. The obtained parameters were utilized in the General-Purpose Photovoltaic Device Model (GPVDM) software to perform numerical simulations of the organic solar cell and Perovskite solar cell. Both Organic solar cells and Perovskite solar cells were designed numerically and through careful observations, electrical parameters like Open circuit Voltage (Voc), Short circuit current (Jsc), Fill Factor (FF), and Power Conversion Efficiency (PCE) were identified. The studies indicate the promising performance of simulated solar cells with SILAR-synthesized ZnO thin film as the ETL.

Patients with reported history of severe iodinated contrast reaction are not uncommon in daily practice. Iodinated contrast is most frequently administered intravenously (IV) for CT scans but is also used intraluminally during urologic procedures and postoperatively to assess for leaks. Providers often are unaware that patients with prior iodinated contrast allergy after IV administration are still at risk for a reaction during intraluminal administration. We present a case of a patient with history of iodinated severe contrast allergy, in which CT cystography using a gadolinium-based-contrast agent was safely performed as an alternative to iodinated-based-cystography to evaluate for a postoperative leak.

BackgroundThe researchers have used mobile phones to assist in monitoring, analyzing, and managing moods to acquire insight into mood patterns. There is a lack of evidence in their use as clinical tools and interventions, which necessitates a comprehensive review and quality assessment to understand barriers and facilitators for app implementation as an impactful clinical intervention. ObjectiveThis review aims to (1) provide an overview of the recent evidence on mobile mood-monitoring apps that are intended for facilitating self-management and support of mental health in children, adolescents, and young people; and (2) investigate the quality of publicly available apps. MethodsThe study will first involve a scoping review of the literature on mood-monitoring apps for children, adolescents, and young people followed by an evaluation of features of the apps available in the marketplace. The scoping review will follow the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and search 6 databases— Embase, CINAHL, PubMed, ACM Digital Library, Scopus, and Springer LNCS—for relevant studies and reviews published in the last 3 years. The author will then screen the references, extract data from the included studies, and analyze them to synthesize the evidence on mood apps. Next, the Apple App Store and Google Play Store will be searched for mood apps. A total of 2 independent reviewers will screen the apps based on eligibility criteria, and disagreements will be resolved through consensus. The features of the selected apps will then be evaluated using the Mobile Health Index and Navigation framework, and descriptive analysis will be used to synthesize the findings. ResultsLiterature search and screening began soon after submission of the protocol and is expected to be completed by September 2024. The app evaluation will be completed by October 2024. ConclusionsCombined, the scoping literature review and app evaluation will provide an in-depth overview of the most recent scientific evidence related to mood apps and the quality of apps actually available for use. International Registered Report Identifier (IRRID)PRR1-10.2196/56400

Introduction Endoscopic transsphenoidal surgery (ETS) is the standard practice in pituitary surgeries. The sellar exposure becomes the main factor which determines the residual disease in ETS. Not many studies can be found in the literature on the influence of anatomical variations of the sphenoid on intraoperative sella exposure.

Introduction The National Early Warning Score (NEWS/2) system was developed to enable the detection and early intervention of patients at risk of clinical deterioration. It has demonstrated good accuracy in identifying imminent critical outcomes but has limitations in its applicability to various patient types and its ability to predict upcoming deterioration beyond 24 hours. Various studies have attempted to improve its predictive accuracy and clinical utility by modifying or adding variables to the standard NEWS/2 system. The purpose of this scoping review is to identify modifications to the NEWS and NEWS2 systems (eg, the inclusion of additional patient demographic, physiological or other characteristics) and how those modifications influence predictive accuracy to provide an evidence base for subsequent improvement of the system.Methods and analysis The review will be structured using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews and the Population, Intervention, Comparator, Outcome, and Study frameworks. Six databases (PubMed, ScienceDirect, Embase, CINAHL, Web of Science and Cochrane Library) will be searched in April 2024 for articles published in English. Article screening and data extraction will be conducted by two independent reviewers, with any conflicts resolved by discussion. The analysis will be descriptive to provide a summary of modifications identified and their influence on the predictive accuracy of NEWS/NEWS 2.Ethics and dissemination Ethical approval is not required as data will be obtained from already published sources. Findings from this study will be disseminated via publication in a peer-reviewed journal.

Background People increasingly rely on online health information for their health-related decision-making. Given the overwhelming amount of information available, the risk of misinformation is high. Health recommender systems, which recommend personalised health-related information or interventions using intelligent algorithms, have the potential to address this issue. Many such systems have been developed and evaluated individually, but there is a need to synthesise the evaluation findings to identify gaps and ensure that future recommender systems are designed to have a positive impact on health or target behaviours.Objective The purpose of this review is to provide an overview of the state of the literature evaluating health recommender systems and highlight lessons learnt, methodological considerations and gaps in current research.Methods and analysis The review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews and the Population, Concept, and Context frameworks. Five databases (PubMED, ACM Digital Library Full-Text Collection, IEEE Xplore, Web of Science and ScienceDirect) will be searched for studies published in English that evaluate at least one health recommender system using search terms following the themes reflecting digital health, recommendation systems and evaluations of efficacy and impact. After using EndNote 21 for initial screening, two independent reviewers will screen the titles, abstracts and full texts of the references, and then extract data from included studies related to the recommender system characteristics, evaluation design and evaluation findings into a predetermined form. A descriptive analysis will be conducted to provide an overview of the literature; key themes and gaps in the literature will be discussed.Ethics and dissemination Ethical approval is not required as data will be obtained from already published sources. Findings from this study will be disseminated via publication in a peer-reviewed journal.

Introduction Literature demonstrates the detrimental impact of discrimination and microaggressions at personal and institutional levels in the health care workplace. Residents in our program requested curricula to help with addressing manifestations of bias. In response, we designed and implemented an adaptable and reproducible 4-hour virtual simulation session aimed at helping residents identify and constructively respond to microaggressions. Methods This curriculum, influenced by a preceding needs assessment, was delivered to 68 senior internal medicine residents. It began with a didactic overview to establish foundational knowledge of bias. This was followed by a workshop focused on strategies to address microaggressions. The session culminated with skills practice in a virtual simulation activity where learners addressed microaggressions as bystanders in realistic case scenarios employing simulated participants. We administered pre- and postevaluation individual key-linked surveys assessing learner confidence in responding to microaggressions. Results A total of 68 residents participated in the curriculum over two academic years, 27 of whom provided complete data for analysis. Overall, there was a statistically significant increase in learner confidence identifying microaggressions. As both a bystander and target/recipient of microaggressions, there were statistically significant increases in learner confidence addressing gender-based microaggressions, race-based microaggressions, and microaggressions reflecting other types of bias. Furthermore, there were statistically significant increases in learner confidence addressing microaggressions in low-acuity contexts, high-acuity contexts, across interprofessional disciplines, with a supervisor, and with a supervisee. Discussion Our virtual experiential curriculum on responding to microaggressions can help increase learner confidence in addressing microaggressions.

Renewable energy can lead to a sustainable future and solar energy is one the primary sources of renewable energy. Solar energy is harvested mainly by photovoltaic plants. Though there are a large number of solar panels, the economic efficiency of solar panels is not that high in comparison to energy production from coal or nuclear matter. The main risk involved in solar plants is the high maintenance cost involved in maintaining the plants. To help reduce this issue, automated solutions using Unmanned Aerial Vehicles (UAVs) and satellite imagery are proposed. In this research work, we propose a novel deep learning architecture for the segmentation of solar plant aerial images, which not only helps in automated solar plant maintenance, but can also be used for the area estimation and extraction of solar panels from an image. Along with this, we also propose a transfer learning-based model for the efficient classification of solar panel damage. Solar panel damage classification has a lot of applications. It can be integrated into monitoring systems, raising alerts when there is severe damage or damage of a certain type. The adaptive UNet model with Atrous Spatial Pyramid Pooling (ASPP) module that performed the dilated convolutions that we proposed achieved an overall accuracy of 98% with a Mean Intersection-Over-Union (IoU) Score of 95% and took under a second to process an image. Our classification model using Visual Geometry Group 19 (VGG19) as the backbone for feature extraction has achieved a classification accuracy of 98% with an F1 score of 99%, thus detecting the five classes of damage, including undamaged solar panels, in an efficient manner.