An observational study of effect of Mullerian anomalies on pregnancy

Background: Mullerian anomalies occur in approximately 3-4% of fertile and infertile women, 5–10% of women with recurrent early pregnancy loss, and up to 25% of women with late first or second-trimester pregnancy loss or preterm delivery. However, due to low prevalence rate and asymptomatic course of the anomalies, Mullerian anomalies remain underdiagnosed and often overlooked as a possible cause of recurrent pregnancy failures, preterm deliveries, IUGR and low birth weight.Methods: Total of 30 cases of Mullerian anomalies with pregnancy, prior diagnosed or incidental during LSCS, were studied for complications during pregnancy, history of gynecological complaints and rate of diagnosis with routine imaging technique.Results: Septate uterus was the most common anomaly seen in this study (36.6%).56.6% were diagnosed incidentally during LSCS despite the fact 26.6% of cases had history of 2 or more abortions and 30% had some or other gynecological complaints previously. 10% of pregnancies ended in abortions, 20% had preterm delivery, 36.6% had malpresentations and there was case of rupture uterus (03.3%).Conclusions: Mullerian anomalies are often asymptomatic or have subtle gynecological symptoms which are often missed by both patient and gynecologists. It is observed that due to the asymptomatic course of Mullerian anomalies, invasive nature of HSG and lack of 1.5 Tesla MRI at many institutes leads to low rate of diagnosis of Mullerian anomalies. Pregnancy with Mullerian anomalies often have preterm delivery, IUGR and malpresentation, so, require proper counselling and close monitoring during antenatal period

Genetic recombination and diversity analysis reveal novel strain of Citrus tristeza virus (Closterovirus tristezae) in Khasi mandarin growing region of Assam

Northeast (NE) India, a hotspot for citrus biodiversity, is facing a serious threat from the Closterovirus tristezae (CTV). A devastating CTV transmitted by the brown citrus aphid (Toxoptera citricida). This pathogen has led to the decline of over one million citrus trees across India, threatening the region’s citrus industry. CTV is characterised by flexuous, filamentous virions (2000×11 nm) and a positive-sense, single-stranded RNA genome (~19.3 kb) encoding 12 open reading frames (ORFs) and approximately 19 putative proteins. To assess the prevalence and genetic diversity of CTV, a systematic survey was conducted in Khasi mandarin (Citrus reticulata) orchards across four districts of Assam: Kamrup Metro, Karbi Anglong, Kamrup Rural and Goalpara. Infected trees exhibited a spectrum of disease symptoms, including decline, chlorosis, leaf yellowing, poor growth and stunting. Disease incidence was determined using Direct Antigen Coated-Enzyme Linked Immunosorbent Assay (DAC-ELISA) and percent disease incidence, revealing 68.2 % overall infection rate. Genetic characterization of 19 CTV isolates, based on a 404-nt fragment of the 5’ ORF1a, unveiled substantial sequence variability, with pairwise nucleotide identities ranging from 85–100 %. Phylogenetic reconstruction grouped these isolates into five distinct genogroups, underscoring significant intra-farm genetic diversity within citrus orchards. Recombination analysis using RDP4 software identified multiple recombinant isolates (BHKM-1, ASKM-1, ASKM-2, MKM-2 and RTKM-1), with BHKM-6 as the major parent and MB-3 as a minor parent contributing to recombination events. These findings provide critical insights into the genetic landscape of CTV in Northeastern (NE) India, emphasising the need for targeted disease management strategies to mitigate further citrus decline

1′-Methyl-4′-phenyl-2′′-sulfanylidene­dispiro­[indoline-3,2′-pyrrolidine-3′,5′′-1,3-thia­zolidine]-2,4′′-dione

The title compound, C20H17N3O2S2, crystallizes with two mol­ecules in the asymmetric unit. The pyrrolo­dine rings have envelope conformations in both mol­ecules, the N atoms deviating by 0.574 (3) and 0.612 (2) Å from the mean planes through the other ring atoms. The 1′-methyl and 4′-phenyl groups on the pyrrolidine rings are substituted in equatorial positions. In the crystal, mol­ecules are linked into a three-dimensional network by N—H⋯O, N—H⋯N and C—H⋯O and N—H⋯π hydrogen bonds

Climate change and health effects in Northwest Alaska

This article provides examples of adverse health effects, including weather-related injury, food insecurity, mental health issues, and water infrastructure damage, and the responses to these effects that are currently being applied in two Northwest Alaska communities

Flower-like strontium molybdate anchored on 3D N-rich reduced graphene oxide aerogel composite: An efficient catalyst for the detection of lethal pollutant nitrobenzene in water samples

Nitrobenzene (NB) is a carcinogenic water pollutant that can have dangerous effects on humans, animals, and the environment even in trace amounts. It can persist in contaminated sites and leach into the adjacent aquatic environment. Therefore, the detection of trace amounts of NB is of great interest. To address this challenge, we have fabricated strontium molybdate microflowers (SrMoO4, SMO MFs) grown on nitrogen-rich, porous three-dimensional (3D) reduced graphene oxide aerogels (SMO/N-rGO) for sensitive detection of NB in water samples. The 3D N-rGO and SMO/N-rGO composites were prepared by simple hydrothermal and precipitation methods. The fabricated SMO/N-rGO composites exhibited a porous and 3D structure with a strong synergistic effect between the SMO MFs and the N-rich porous rGO sheets with open voids that facilitate the diffusion of NB. The electrochemical detection of NB at the SMO/N-rGO modified electrode was significantly enhanced. Using amperometry (i-t), the modified SMO/N-rGO sensor was shown to have two linear response ranges in the sensing of NB, with the lower linear concentration range from 7.1 nM to 1.0 mM and the higher linear concentration range varying from 1.1 mM to 2.5 mM. In addition, the limit of detection (LOD) was calculated to be 2.1 nM using the amperometric (i-t) technique. Common nitro derivatives, biomolecules, and cations often found in water systems had no influence on the detection of NB. At the same time, a good recovery of 96.1–99.6% was obtained for real-time monitoring analysis in tap and lake water samples. In this work, new electrochemical sensors for monitoring various pollutants are developed based on anchoring conductive metal oxide electrocatalysts on porous 3D carbon aerogels

Global assessment of production benefits and risk reduction in agroforestry during extreme weather events under climate change scenarios

Climate change and extreme weather events are threatening agricultural production worldwide. The anticipated increase in atmospheric temperature may reduce the potential yield of cultivated crops. Agroforestry is regarded as a climate-resilient system that is profitable, sustainable, and adaptable, and has strong potential to sequester atmospheric carbon. Agroforestry practices enhance agroecosystems’ resilience against adverse weather conditions via moderating extreme temperature fluctuations, provisioning buffers during heavy rainfall events, mitigating drought periods, and safeguarding land resources from cyclones and tsunamis-type events. Therefore, it was essential to comprehensively analyze and discuss the role of agroforestry in providing resilience during extreme weather situations. We hypothesized that integrating trees in to the agro-ecosystems could increase the resilience of crops against extreme weather events. The available literature showed that the over-story tree shade moderates the severe temperature (2–4°C) effects on understory crops, particularly in the wheat and coffee-based agroforestry as well as in the forage and livestock-based silvipasture systems. Studies have shown that intense rainstorms can harm agricultural production (40–70%) and cause waterlogging. The farmlands with agroforestry have been reported to be more resilient to heavy rainfall because of the decrease in runoff (20–50%) and increase in soil water infiltration. Studies have also suggested that drought-induced low rainfall damages many crops, but integrating trees can improve microclimate and maintain crop yield by providing shade, windshield, and prolonged soil moisture retention. The meta-analysis revealed that tree shelterbelts could mitigate the effects of high water and wind speeds associated with cyclones and tsunamis by creating a vegetation bio-shield along the coastlines. In general, existing literature indicates that implementing and designing agroforestry practices increases resilience of agronomic crops to extreme weather conditions increasing crop yield by 5–15%. Moreover, despite its widely recognized advantages in terms of resilience to extreme weather, the systematic documentation of agroforestry advantages is currently insufficient on a global scale. Consequently, we provide a synthesis of the existing data and its analysis to draw reasonable conclusions that can aid in the development of suitable strategies to achieve the worldwide goal of adapting to and mitigating the adverse impacts of climate change

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques