Short-Term Memory Deficit Associates with miR-153-3p Upregulation in the Hippocampus of Middle-Aged Mice

The early stages of ageing are a critical time window in which the ability to detect and identify precocious molecular and cognitive markers can make the difference in determining a healthy vs unhealthy course of ageing. Using the 6-different object task (6-DOT), a highly demanding hippocampal-dependent recognition memory task, we classified a population of middle-aged (12-month-old) CD1 male mice in Impaired and Unimpaired based on their short-term memory. This approach led us to identify a different microRNAs expression profile in the hippocampus of Impaired mice compared to Unimpaired ones. Among the dysregulated microRNAs, miR-153-3p was upregulated in the hippocampus of Impaired mice and appeared of high interest for its putative target genes and their possible implication in memory-related synaptic plasticity. We showed that intra-hippocampal injection of the miR-153-3p mimic in adult (3-month-old) mice is sufficient to induce a short-term memory deficit similar to that observed in middle-aged Impaired mice. Overall, these findings unravel a novel role for hippocampal miR-153-3p in modulating short-term memory that could be exploited to prevent early cognitive deficits in ageing.

Archimedes optimisation algorithm quantum dilated convolutional neural network for road extraction in remote sensing images

Roads are closely intertwined with human existence, and the process of extracting road networks has emerged as the most prominent task in remote sensing (RS). The automated road interpretation process of remote sensing images (RSI) efficiently acquires road network data at a reduced expense in comparison to the traditional visual interpretation of RSI. However the manifestation of RSI is completely distinct because of the great difference in length, width, material, and shape of road networks in dissimilar areas. Thus, the extraction of road network data in RSI is still a complex issue. In recent times, DL-based approaches have projected a famous development in image segmentation outcomes, but a lot of them still could not retain boundary data and attain high-resolution road segmentation maps while processing the RSI. Traditional convolutional neural networks (CNNs) demonstrate impressive performance in road extract tasks; however, they frequently encounter difficulties in capturing intricate details and contextual information. The study introduces a novel method, named Archimedes Optimisation Algorithm, Quantum Dilated Convolutional Neural Network for Road Extraction (AOA-QDCNNRE), to tackle the challenges encountered in remote sensing images. The AOA-QDCNNRE technique aims to generate a high-resolution road segmentation map using DL with a hyperparameter tuning process. The AOA-QDCNNRE technique primarily relies on the QDCNN model, which integrates quantum technology (QC) with dilated convolutions to augment the network's capacity to capture local as well as global contextual information. In addition, the incorporation of the dilated convolutional technique effectively enhances the receptive field without sacrificing spatial resolution, enabling the extraction of precise road features. To develop the road extraction outcomes of the QDCNN approach, the AOA-based hyperparameter tuning process can be exploited. The AOA-QDCNNRE system's simulation results can be tested on benchmark databases, and the results indicate that the AOA-QDCNNRE method surpasses recent algorithms.

Empowering coffee farming using counterfactual recommendation based RNN driven IoT integrated soil quality command system

Soil health is essential for whirling stale soil into rich coffee-growing land. By keeping healthy soil, coffee producers may improve plant growth, leaf health, buds, cherry and bean quality, and yield. Traditional soil monitoring is tedious, time-consuming, and error-prone. Enhancing the monitoring system using AI-based IoT technologies for quick and precise changes. Integrated soil fertility control system to optimize soil health, maximize efficiency, promote sustainability, and prevent crop threads using real-time data analysis to turn infertile land into fertile land. The RNN-IoT approach uses IoT sensors in the coffee plantation to collect real-time data on soil temperature, moisture, pH, nutrient levels, weather, CO2 levels, EC, TDS, and historical data. Data transmission using a wireless cloud platform. Testing and training using recurrent neural networks (RNNs) and gated recurrent units gathered data for predicting soil conditions and crop hazards. Researchers are carrying out detailed qualitative testing to evaluate the proposed RNN-IoT approach. Utilize counterfactual recommendations for developing alternative strategies for irrigation, fertilization, fertilizer regulation, and crop management, taking into account the existing soil conditions, forecasts, and historical data. The accuracy is evaluated by comparing it to other deep learning algorithms. The utilization of the RNN-IoT methodology for soil health monitoring enhances both efficiency and accuracy in comparison to conventional soil monitoring methods. Minimized the ecological impact by minimizing water and fertilizer utilization. Enhanced farmer decision-making and data accessibility with a mobile application that provides real-time data, AI-generated suggestions, and the ability to detect possible crop hazards for swift action.

Dipper throated optimization with deep convolutional neural network-based crop classification for remote sensing image analysis

Problem

With the rapid advancement of remote sensing technology is that the need for efficient and accurate crop classification methods has become increasingly important. This is due to the ever-growing demand for food security and environmental monitoring. Traditional crop classification methods have limitations in terms of accuracy and scalability, especially when dealing with large datasets of high-resolution remote sensing images. This study aims to develop a novel crop classification technique, named Dipper Throated Optimization with Deep Convolutional Neural Networks based Crop Classification (DTODCNN-CC) for analyzing remote sensing images. The objective is to achieve high classification accuracy for various food crops.

Methods

The proposed DTODCNN-CC approach consists of the following key components. Deep convolutional neural network (DCNN) a GoogleNet architecture is employed to extract robust feature vectors from the remote sensing images. The Dipper throated optimization (DTO) optimizer is used for hyper parameter tuning of the GoogleNet model to achieve optimal feature extraction performance. Extreme Learning Machine (ELM): This machine learning algorithm is utilized for the classification of different food crops based on the extracted features. The modified sine cosine algorithm (MSCA) optimization technique is used to fine-tune the parameters of ELM for improved classification accuracy.

Results

Extensive experimental analyses are conducted to evaluate the performance of the proposed DTODCNN-CC approach. The results demonstrate that DTODCNN-CC can achieve significantly higher crop classification accuracy compared to other state-of-the-art deep learning methods.

Conclusion

The proposed DTODCNN-CC technique provides a promising solution for efficient and accurate crop classification using remote sensing images. This approach has the potential to be a valuable tool for various applications in agriculture, food security, and environmental monitoring.

Computational linguistics based text emotion analysis using enhanced beetle antenna search with deep learning during COVID-19 pandemic

Computational intelligence and nature-inspired computing have changed the way biologically and linguistically driven computing paradigms are made. In the last few decades, they have been used more and more to solve optimisation problems in the real world. Computational linguistics has its roots in linguistics, but most of the studies being done today are led by computer scientists. Data-driven and machine-learning methods have become more popular than handwritten language rules, which shows this shift. This study uses a new method called Computational Linguistics-based mood Analysis using Enhanced Beetle Antenna Search with deep learning (CLSA-EBASDL) to tackle the important problem of mood analysis during the COVID-19 pandemic. We sought to determine how people felt about the COVID-19 pandemic by studying social media texts. The method is made up of three main steps. First, data pre-processing changes raw data into a shape that can be used. After that, word embedding is done using the 'bi-directional encoder representations of transformers (BERT) process. An attention-based bidirectional long short-term memory (ABiLSTM) network is at the heart of mood classification. The Enhanced Beetle Antenna Search (EBAS) method, in particular, fine-tunes hyperparameters so that the ABiLSTM model works at its best. Many tests show that the CLSA-EBASDL method works better than others. Comparative studies show that it works, making it the best method for analysing opinion during the COVID-19 pandemic.

Predicting the Splash of a Drop Impacting a Thin Liquid Film

An experimental study is carried out to investigate droplet-film interactions when a drop impinges on a thin stagnant film of the same liquid. The impacting drop causes either liquid deposition or splash, consisting of prompt generation of secondary drops or a delayed process. By varying the drop diameter and impact velocity, measurements are made to characterize the phenomena using five different liquids that are chosen to cover a wide range of liquid properties (viscosity and surface tension). The drop impact dynamics are captured with a high-speed digital camera with real-time, high-resolution image processing. The drop-splash threshold is found to scale with inertial and viscous forces, or Reynolds number (Re), as well as capillary forces, as described by the balance of gravitational and interfacial tension forces, or Bond number (Bo); fluid properties are described by their Morton number (Mo). A correlation, functionally expressed as Re = ϕ(Bo,Mo), is devised to determine the splash/no-splash (or deposition) boundary, and the predictions for the splash/no-splash outcomes agree well with the experimental outcomes as well as those readily available in the literature.

Environmental remediation of hazardous pollutants using MXene-perovskite-based photocatalysts: A review

Photocatalysis utilizing semiconductors offer a cost-effective and promising solution for the removal of pollutants. MXene and perovskites, which possess desirable properties such as a suitable bandgap, stability, and affordability, have emerged as a highly promising material for photocatalytic activity. However, the efficiency of MXene and perovskites is limited by their fast recombination rates and inadequate light harvesting abilities. Nonetheless, several additional modifications have been shown to enhance their performance, thereby warranting further exploration. This study delves into the fundamental principles of reactive species for MXene-perovskites. Various methods of modification of MXene-perovskite-based photocatalysts, including Schottky junction, Z-scheme and S-scheme are analyzed with regard to their operation, differences, identification techniques and reusability. The assemblance of heterojunctions is demonstrated to enhance photocatalytic activity while also suppressing charge carrier recombination. Furthermore, the separation of photocatalysts through magnetic-based methods is also investigated. Consequently, MXene-perovskite-based photocatalysts are seen as an exciting emerging technology that necessitates further research and development.

Xpert Carba-R Assay on Flagged Blood Culture Samples: Clinical Utility in Intensive Care Unit Patients with Bacteremia Caused by Enterobacteriaceae

Introduction and background

Rapid molecular diagnostics to predict carbapenem resistance well before the availability of routine drug sensitivity testing (DST) can serve as an antimicrobial stewardship tool in the context of high rates of Carbapenem-resistant Enterobacteriaceae (CRE).

Materials and methods

A retrospective observational study of patients more than 18 years of age on whom Xpert Carba-R (FDA approved for rectal swab specimen) was done on gram-negative bacteria (GNB) flagged blood culture samples, in an Indian intensive care unit between January 2015 and November 2018. We analyzed the performance of Xpert Carba-R in comparison with routine DST.

Results

A total of 164 GNBs were isolated from 160 patients. Klebsiella pneumoniae and Escherichia coli were the predominant isolates. Carba-R was positive in 35.36% of samples and 45.34% were carbapenem-resistant (CR) on routine DST. The distribution of the CR gene was: Oxacillinase (OXA) (50%), NDM (32.7%) followed by OXA and NDM co-expression (15.51%). The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, positive predictive value, and negative predictive value of Carba-R were 90.74, 93.15, 13.25, 0.10, 83.58 and 96.31% for Enterobacteriaceae. The median time to obtain the Carba-R report was 30 hours 34 minutes vs 74 hours and 20 minutes for routine DST. Based on the Carba-R report, 9.72% of patients had escalation and 27.08% had de-escalation of antibiotics.

Conclusion

Xpert Carba-R serves as a rapid diagnostic tool for predicting carbapenem resistance in intensive care unit patients with bacteremia caused by Enterobacteriaceae.

How to cite this article

Rajendran S, Gopalakrishnan R, Tarigopula A, Kumar DS, Nambi PS, Sethuraman N, et al. Xpert Carba-R Assay on Flagged Blood Culture Samples: Clinical Utility in Intensive Care Unit Patients with Bacteremia Caused by Enterobacteriaceae. Indian J Crit Care Med 2023;27(9):655-662.

Penguin search optimization algorithm with multi-agent reinforcement learning for disease prediction and recommendation model

Multi-agent reinforcement learning (MARL) is a generally researched approach for decentralized controlling in difficult large-scale autonomous methods. Typical features create RL system as an appropriate candidate to develop powerful solutions in variation of healthcare fields, whereas analyzing decision or treatment systems can be commonly considered by a prolonged and sequential process. This study develops a new Penguin Search Optimization Algorithm with Multi-agent Reinforcement Learning for Disease Prediction and Recommendation (PSOAMRL-DPR) model. This research aimed to use a unique PSOAMRL-DPR algorithm to forecast diseases based on data collected from networks and the cloud by a mobile agent. The major intention of the proposed PSOAMRL-DPR algorithm is to identify the presence of disease and recommend treatment to the patient. The model manages the agent container with different mobile agents and fetched data from dissimilar locations of the network as well as cloud. For disease detection and prediction, the PSOAMRL-DPR technique exploits deep Q-network (DQN) technique. In order to tune the hyperparameters related to the DQN technique, the PSOA technique is used. The experimental result analysis of the PSOAMRL-DPR technique is validated on heart disease dataset. The simulation values demonstrate that the PSOAMRL-DPR technique outperforms the other existing methods.

Student Psychology based optimized routing algorithm for big data clustering in IoT with MapReduce framework

Clustering plays a fundamental task in the process of data mining, which remains more demanding due to the ever-increasing dimension of accessible datasets. Big data is considered more populous as it has the ability to handle various sources and formats of data under numerous highly developed technologies. This paper devises a robust and effective optimization-based Internet of Things (IoT) routing technique, named Student Psychology Based Optimization (SPBO) -based routing for the big data clustering. When the routing phase is done, big data clustering is carried out using the Deep Fractional Calculus-Improved Invasive Weed Optimization fuzzy clustering (Deep FC-IIWO fuzzy clustering) approach. Here, the Mapreduce framework is used to minimizing the over fitting issues during big data clustering. The process of feature selection is performed in the mapper phase in order to select the major features using Minkowski distance, whereas the clustering procedure is carried out in the reducer phase by Deep FC-IIWO fuzzy clustering, where the FC-IIWO technique is designed by the hybridization of Improved Invasive Weed Optimizer (IIWO) and Fractional Calculus (FC). The developed SPBO-based routing approach achieved effective performance in terms of energy, clustering accuracy, jaccard coefficient, rand coefficient, computational time and space complexity of 0.605 J, 0.935, 0.947, 0.954, 2100.6 s and 72KB respectively.

Ex Situ In Vivo Technique of Spleen-Preserving Splenic Hilar Lymph Node Dissection in Operable Proximal Gastric Adenocarcinoma

D2 gastrectomy is the globally accepted standard surgical procedure for operable gastric cancer, and lymph node (LN) dissection is considered as the critical part of radical surgery and closely related to the prognosis. The splenic hilar LN (SHLN) or level 10 are to be removed during standard D2 total gastrectomy. In situ and ex situ spleen-preserving lymphadenectomies have been the most common dissection approaches for SHLNs. No study exists which compares the outcomes of these techniques in Indian population. This study is aimed to analyse the operative outcomes of ex situ in vivo technique of spleen-preserving splenic hilar lymph node dissection in patients who underwent D2 total gastrectomy for operable proximal gastric cancer in comparison with in situ in vivo technique of splenic hilar lymph node dissection. We reviewed prospectively collected data from 17 patients with operable proximal gastric cancer between September 2016 and April 2019 who underwent D2 total gastrectomy with splenic hilar lymph node dissection and studied the preoperative demographic factors, operative and postoperative outcomes comparing the different operative techniques. Patients with oesophago-gastric junction involvement, direct splenic or other adjacent organ invasion requiring multivisceral resection and gastric stump carcinoma were excluded. Overall, 17 patients underwent D2 total gastrectomy for operable gastric cancer. Mean age of presentation was 54.7 years including 13 males and 4 females. Five patients had middle third and 12 patients had upper third cancer. All patients had splenic hilar nodal clearance as follows: in situ - 14 and ex situ - 3 patients. R0 resection was achieved in all patients. Lymph node harvest tends to be higher with lower operative time and blood loss in patients with ex situ technique compared to in situ technique with similar morbidity. Ex situ in vivo technique of spleen-preserving splenic hilar lymph node dissection can be considered as both safe and feasible procedure for operable proximal gastric cancer patients in experienced centres to achieve better lymph node yield with no significant increase in morbidity.

Application of Pharmacokinetic/Pharmacodynamic Modeling to Bridge Mouse Antitumor Efficacy and Monkey Toxicology Data for Determining the Therapeutic Index of an Interleukin-10 Fc Fusion Protein

Pharmacokinetic/pharmacodynamic (PK/PD) modeling was performed to quantitatively integrate preclinical pharmacology and toxicology data for determining the therapeutic index (TI) of an interleukin-10 (IL-10) fragment crystallizable (Fc) fusion protein. Mouse Fc fused with mouse IL-10 (mFc-mIL-10) was studied in mice for antitumor efficacy, and the elevation of interleukin-18 (IL-18) was examined as a PD biomarker. The in vivo mFc-mIL-10 EC₅₀ for the IL-18 induction was estimated to be 2.4 nM, similar to the in vitro receptor binding affinity (K_d) of 3.2 nM. The IL-18 induction was further evaluated in cynomolgus monkeys, where the in vivo induction EC₅₀ by a human IL-10 human Fc-fusion protein (hFc-hIL-10) was 0.08 nM vs. 0.3 nM measured as the in vitro K_d. The extent of the IL-18 induction correlated with mouse antitumor efficacy and was used to connect mouse efficacy to that in monkeys. The PD-based efficacious dose projected in monkeys was comparable to the results obtained using a PK-based method in which mouse efficacious exposure was targeted and corrected for affinity differences between the species. Furthermore, PK/PD relationships were developed for anemia and thrombocytopenia in monkeys treated with hFc-hIL-10, with thrombocytopenia predicted to be dose-limiting toxicity. Using quantitative pharmacology and toxicology information obtained through modeling work in the same species, the TI of hFc-hIL-10 in monkeys was determined to be 2.4 (vs. PD-based efficacy) and 1.2–3 (vs. PK-based efficacy), indicating a narrow safety margin. The model-based approaches were proven valuable to the developability assessment of the IL-10 Fc-fusion protein.

MapReduce-based big data classification model using feature subset selection and hyperparameter tuned deep belief network

In recent times, big data classification has become a hot research topic in various domains, such as healthcare, e-commerce, finance, etc. The inclusion of the feature selection process helps to improve the big data classification process and can be done by the use of metaheuristic optimization algorithms. This study focuses on the design of a big data classification model using chaotic pigeon inspired optimization (CPIO)-based feature selection with an optimal deep belief network (DBN) model. The proposed model is executed in the Hadoop MapReduce environment to manage big data. Initially, the CPIO algorithm is applied to select a useful subset of features. In addition, the Harris hawks optimization (HHO)-based DBN model is derived as a classifier to allocate appropriate class labels. The design of the HHO algorithm to tune the hyperparameters of the DBN model assists in boosting the classification performance. To examine the superiority of the presented technique, a series of simulations were performed, and the results were inspected under various dimensions. The resultant values highlighted the supremacy of the presented technique over the recent techniques.

Complete resection of the iliac vascular system during pelvic exenteration: an evolving surgical approach to lateral compartment excision

Tumour infiltration of the lateral pelvic compartment has previously been associated with the highest rate of involved resection margins and carries significant risk of morbidity. In this study, consecutive patients undergoing pelvic exenteration at a single centre between 1994 and 2019 who required en bloc resection of the common or external iliac artery or vein were included.The results demonstrate that complete resection of the iliac vascular system, including resection and reconstruction of the common and external iliac vessels, can be performed safely during pelvic exenteration with oncological outcomes comparable to more central tumours.

Effect of varietal attributes on the adoption of an orange-fleshed sweetpotato variety in Upper East and Northern Ghana

Despite sustained economic growth and reduction in some of forms of malnutrition, Ghana still faces a national prevalence rate of 20.8% vitamin A deficiency (VAD) among for children 6-59 months old. Orange-fleshed sweetpotato (Ipomoea batatas L.) (OFSP) can significantly improve vitamin A intake and contribute toward reducing VAD, especially in Northern Ghana where VAD is 31% among young children. Several poverty and nutrition projects in Ghana have promoted the use of OFSP for its health benefits. This study assesses the effect of three varietial attributes on adoption of the first released OFSP variety in Northern Ghana namely, Apomuden. The study concluded that sweetness, taste and dry matter have joint significant effects on adoption of an OFSP variety. The positive and negative traits highlighted will inform the on-going breeding effort.

Perioperative intravenous contrast administration and the incidence of acute kidney injury after major gastrointestinal surgery: prospective, multicentre cohort study

BACKGROUND

This study aimed to determine the impact of preoperative exposure to intravenous contrast for CT and the risk of developing postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery.

METHODS

This prospective, multicentre cohort study included adults undergoing gastrointestinal resection, stoma reversal or liver resection. Both elective and emergency procedures were included. Preoperative exposure to intravenous contrast was defined as exposure to contrast administered for the purposes of CT up to 7 days before surgery. The primary endpoint was the rate of AKI within 7 days. Propensity score-matched models were adjusted for patient, disease and operative variables. In a sensitivity analysis, a propensity score-matched model explored the association between preoperative exposure to contrast and AKI in the first 48 h after surgery.

RESULTS

A total of 5378 patients were included across 173 centres. Overall, 1249 patients (23·2 per cent) received intravenous contrast. The overall rate of AKI within 7 days of surgery was 13·4 per cent (718 of 5378). In the propensity score-matched model, preoperative exposure to contrast was not associated with AKI within 7 days (odds ratio (OR) 0·95, 95 per cent c.i. 0·73 to 1·21; P = 0·669). The sensitivity analysis showed no association between preoperative contrast administration and AKI within 48 h after operation (OR 1·09, 0·84 to 1·41; P = 0·498).

CONCLUSION

There was no association between preoperative intravenous contrast administered for CT up to 7 days before surgery and postoperative AKI. Risk of contrast-induced nephropathy should not be used as a reason to avoid contrast-enhanced CT.

2170. Xpert Carba-R Assay on Flagged Blood Culture Samples: Clinical Utility in Intensive Care Unit Patients with Gram-Negative Bacteremia

Background

Rapid molecular diagnostics that predict carbapenem resistance (CR) well before the availability of routine drug sensitivity testing (DST) can serve as an antimicrobial stewardship tool in the context of high rates of carbapenem-resistant enterobacteriaceae (CRE).

Methods

A retrospective observational study of patients more than 18 years of age on whom Xpert Carba-R (FDA approved for rectal swab specimen) was done on Gram-negative bacteria (GNB) flagged blood culture samples, in a tertiary care Indian intensive care unit between January 2015 and November 2018.

Results

The study included 160 patients who had a median (Sequential Organ Failure Assessment) SOFA score of 16. A total of 164 GNB were isolated with 4 patients having polymicrobial bacteremia. Klebsiella pneumoniae and Escherichia coli were the predominant isolates (Figure 1). Carba-R was positive in 58/164 (35.36%) samples (Figure 2) and 73/161 isolates (45.34%) were CR (after excluding intrinsic CR organisms) on routine DST. The distribution of CR genes overall was: OXA-48 like (29/58–50%), followed by NDM (19/58–32.7%), followed by OXA-48 and NDM co-expression (9/58–15.51%) and in individual groups as in Figure 3. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, positive predictive value (PPV) and negative predictive value (NPV) of Carba-R vs. routine DST were 90.74%, 93.15%, 13.25, 0.10, 83.58% and 96.31% for enterobacteriaceae (Figure 4). The median time to obtaining the Carba-R report was 30 hours 34 minutes vs. 74 hours and 20 minutes for carbapenem resistance on routine DST. Based on the Carba-R report, 9.72% of patients had escalation of antibiotics (e.g., colistin) to cover CR organisms and 27.08% had de-escalation from CR cover that had already been started.

Conclusion

Xpert Carba-R serves as a rapid diagnostic tool for CR Gram-negative bacteremia, particularly hospital-acquired enterobacteriaceae in intensive care units with a high CRE prevalence. It assists in initiating early appropriate therapy and early institution of infection control measures in these patients, as well as in de-escalation of colistin in patients without carbapenem resistance. We recommend that clinicians consider routinely utilizing this test in this setting.

Disclosures

All authors: No reported disclosures.

Influence of Ni Doping in SnO₂ Nanoparticles with Enhanced Visible Light Photocatalytic Activity for Degradation of Methylene Blue Dye

In this article, Nickel doped rutile structure tin oxide (SnO₂) nanoparticles have been prepared by simple chemical co-precipitation method and prepared samples were characterized by Powder X-ray Diffraction, Fourier transform infrared Spectroscopy, Microraman analysis, Photoluminescene Spectroscopy, UV-Visible Spectroscopy, Energy dispersive analysis and Field emission scanning electron microscope. XRD studies revealed the single phase tetragonal rutile structure with space group of P42/mnm. The average crystallite size of the particles was decreased from 27 to 22 nm with increasing Ni doping concentration. FTIR spectra confirmed the presence of various bands such as O-H, C-H, Sn-O-Sn. Raman modes E_g, A_1g and B_2g were assigned at 478, 630 and 740 cm^-1 which confirmed the single phase of pure and Ni doped SnO₂ nanoparticles. The photoluminescence spectra confirmed that the defect related emissions increased with increasing of Ni concentration. The UV absorption spectra showed that the absorption of the particles decreased with increasing Ni concentration and the band gap values decreased from 3.7 to 3.4 eV. EDX spectra confirmed the presence of Sn, Ni, O in pure and doped samples. The photocatalytic activity of the pure and Ni doped SnO₂ nanoparticles were analyzed by using methylene blue dye under visible light irradiation. It is concluded Ni (7%) doped SnO₂ nanoparticles have higher degradation efficiency compared to pure SnO₂ nanoparticles.

Type B choledochocele vs duodenal duplication cyst: a diagnostic dilemma and its management: a case report

INTRODUCTION

Duplication cyst of the alimentary tract is a rare congenital anomaly. Duodenal duplication cyst accounts for less than 5% overall. These entities rarely present in adults. They are often mistaken as choledochoceles. Management is most often complete excision, but it is individualized to the particular case.

CASE PRESENTATION

A 22-year-old woman was admitted to our hospital with a history of intermittent colicky right hypochondrial pain not relieved by any medications for the past 3 months. Initially, she was given proton pump inhibitors, but her pain was not relieved. Further evaluation was done, and preoperative imaging showed a cyst in the second part of the duodenum. Magnetic resonance imaging revealed it as a choledochocele, but duodenal duplication cyst was kept in the differential diagnosis. Further ultrasound identified it to be a duplication cyst. After failed endotreatment, the patient was successfully managed with partial excision and marsupialization.

CONCLUSION

Duodenal duplication cyst is uncommon and rarely diagnosed in adults. Duplications in the duodenum should always be a part of the differential diagnosis, especially in cystic lesions. Ultrasonogram of the cyst might lead to the proper diagnosis. Surgery is the treatment of choice if endotherapy is not successful.

Caudal regression syndrome in a fetus of a glucokinase-maturity-onset diabetes of the young pregnancy

BACKGROUND

Glucokinase-maturity-onset diabetes of the young (GCK-MODY) is a form of diabetes caused by heterozygous inactivating mutations in the GCK gene. Affected individuals maintain their fasting glucose levels at a higher set point (5.4-8.3 mmol/l) than the general population. Hyperglycaemia in women with Type 1 or Type 2 diabetes is known to confer increased risk of fetal congenital abnormalities. The association between GCK-MODY and congenital abnormalities, however, remains uncertain.

CASE REPORT

A 35-year-old woman in her third pregnancy was diagnosed with gestational diabetes at 13 weeks' gestation (fasting blood glucose 6.0 mmol/L, 1-h blood glucose 9.2 mmol/l, 2-h blood glucose 7.3 mmol/l). The morphology scan at 19+2 weeks' gestation showed a Type III sacral agenesis. The woman elected to terminate the pregnancy. Her postpartum oral glucose tolerance test was suggestive of GCK-MODY (fasting blood glucose 7.4 mmol/l, 1-h blood glucose 9.3 mmol/l, 2-h blood glucose 7.3 mmol/l). Mutation analysis of the GCK gene identified a novel heterozygous GCK missense mutation, p.V199M, classified as likely pathogenic, providing molecular confirmation of the suspected GCK-MODY diagnosis.

DISCUSSION

Sacral agenesis is a rare form of sacral abnormality affecting 0.005% to 0.1% of pregnancies. It is a subtype of the caudal regression sequence, a cardinal feature of diabetic embryopathy. This case raises the question as to whether hyperglycaemia in GCK-MODY may increase the risk of fetal caudal regression syndrome as reported in women with pre-existing diabetes mellitus. Improved diagnostic rates of GCK-MODY, and MODY registers that include pregnancy outcomes, are important to further elucidate risk of congenital abnormalities in GCK-MODY.

Hydronephrotic kidney with multiple extra-renal calyces

Extra-renal calyces are a rare anomaly of the renal collecting system, characterised by the presence of calyces and pelvis outside the renal parenchyma. It may also be associated with other anomalies, such as renal ectopia, fusion and malrotation. We describe an unusual case of extra-renal calyces with six long calyces arising from a suspected multicystic dysplastic kidney that was successfully managed laparoscopically.

RNA interference in mosquito: understanding immune responses, double-stranded RNA delivery systems and potential applications in vector control

RNA interference (RNAi) refers to the process of post-transcriptional silencing of cellular mRNA by the application of double-stranded RNA (dsRNA). RNAi strategies have been widely employed to regulate gene expression in plants and animals including insects. With the availability of the full genome sequences of major vector mosquitoes, RNAi has been increasingly used to conduct genetic studies of human pathogens in mosquito vectors and to study the evolution of insecticide resistance in mosquitoes. This review summarizes the recent progress in our understanding of mosquito-pathogen interactions using RNAi and various methods of dsRNA delivery in mosquitoes at different stages. We also discuss potential applications of this technology to develop novel tools for vector control.

Chest radiography in acute aortic syndrome: pearls and pitfalls

Acute aortic syndrome is a group of life-threatening diseases of the thoracic aorta that usually present to the emergency department. It includes aortic dissection, aortic intramural hematoma, and penetrating aortic ulcer. Rare aortic pathologies of aorto-esophageal fistula and mycotic aneurysm may also be included in this list. All these conditions require urgent treatment with complex clinical care and management. Most patients who present with chest pain are evaluated with a chest radiograph in the emergency department. It is important that maximum diagnostic information is extracted from the chest radiograph as certain signs on the chest radiograph are extremely useful in pointing towards the diagnosis of acute aortic syndrome.

Lithium Ion Conduction in PVdC-co-AN Based Polymer Blend Electrolytes Doped with Different Lithium Salts

Polymer electrolytes prepared by the complexation of lithium salts with poly(ethylene oxide) (PEO) and poly(vinylidene chloride-co-acrylonitrile) (PVdC-co-AN) will be of great use as separators in lithium polymer batteries. The amorphous nature of the blend electrolyte shows that the conductivity increases by the addition of lithium salts. The presence of C≡N and C=N in PVdC-co-AN are confirmed from the Fourier transform infrared studies. Among the various lithium salts studied, lithium trifluoro methane sulfonoimide [LiN(CF3SO2)2] based electrolyte exhibits the highest ionic conductivity of the order of 0.265 × 10−5 Scm−1 at room temperature. The sample having a maximum ionic conductivity PEO(80 wt%)/PVdC-co-AN(20 wt%)/LiN(CF3SO2)2(8 wt%) is supported by the lower optical band gap in UV-Visible analysis and low intensity in luminescence studies. Two and three dimensional topographic images of the above sample reveal the presence of micropores. Thermal stability of the prepared electrolytes is studied by thermo gravimetric/differential thermal analysis. Using differential scanning calorimetric analysis, the minimum glass transition temperature (30°C) is observed for the sample doped with LiN(CF3SO2)2. The cyclic voltammetric studies reveal the strong capacitive behavior of the prepared polymer electrolytes. The electrochemical stability windows for the prepared samples are observed using linear sweep voltammetry.