A study of pregnancy related acute kidney injury and its outcome at a tertiary care centre, civil hospital, Ahmadabad, Gujarat, India

Background: Pregnancy related acute kidney injury (PRAKI) is acute kidney injury occurring during pregnancy, labour, delivery, and/or postpartum period. Proper management of PRAKI is challenging because (i) both maternal and fetal health must be considered and (ii) the cardiovascular and renal adaptations of pregnancy add to the complexity of diagnosis and management. A multi discipilinary team is often needed to optimize all aspects of the pregnant women’s care.Methods: To study association and contributing factors in pregnancy related Acute Kidney injury, a retrospective study of 39 cases of acute kidney injury complicating pregnancies was carried out in department of obstetrics and gynaecology, B. J. Medical college over a period of 6 months, and results were studied and analysed. Etiological-factors, associated liver pathology, coagulation abnormality, thrombocytopenia, sepsis, recovery status and fetomaternal outcome were studied and results were tabulated. AKI was analysed in terms of maximal stage of renal injury attained as per risk, injury, failure, loss of function, and end-stage renal disease (RIFLE) criteria.Results: The incidence of ARF in pregnancy was found to be 0.3%. Hypertension and its related complications were the most common causative factor. 59.5% of cases required hemodialysis and except for 6 cases (14.3%) all had complete or at least partial recovery from failure.Conclusions: AKI complicating pregnancies are not uncommon in tertiary care centres. If recognized and treated promptly, recovery is assured in majority of 85.7% of cases. Early identification and prompt management of pre-eclampsia and sepsis can prevent majority of ARF cases

Quantum Zeno effect: a qutrit controlled by a qubit

For a three-level system monitored by an ancilla, we show that quantum Zeno effect can be employed to control quantum jump for error correction. Further, we show that we can realize cNOT gate, and effect dense coding and teleportation. We believe that this work paves the way to generalize the control of a qudit

Double-toric code

We construct a double-toric surface code by exploiting the planar tessellation using a rhombus-shaped tile. With n data qubits, we are able to encode at least n/3 logical qubits or quantum memories. By a suitable arrangement of the tiles, the code achieves larger distances, leading to significant error-correcting capability. We demonstrate the robustness of the logical qubits thus obtained in the presence of external noise. We believe that the optimality of the code presented here will pave the way for design of efficient scalable architectures

Quantum error correction beyond the toric code: dynamical systems meet encoding

We construct surface codes corresponding to genus greater than one in the context of quantum error correction. The architecture is inspired by the topology of invariant integral surfaces of certain non-integrable classical billiards. Corresponding to the fundamental domains of rhombus and square torus billiard, surface codes of genus two and five are presented here. There is significant improvement in encoding rates and code distance, in addition to immunity against noise

Indian Diabetes Risk Score (IDRS): An effective tool to screen undiagnosed diabetes

Background: Diabetes is an insidious public health problem. India has the second largest number of adults living with diabetes worldwide (77 million). Indian Diabetes Risk Score (IDRS) is a simple, cost-effective and feasible tool for mass screening programme at community level. Aim & Objective: To assess diabetes risk in adults aged 30 years and above and to identify high risk subjects for screening undiagnosed diabetes in an urban population of Meerut. Settings and Design: Community based cross-sectional study. Methods and Material: All adults who were ?30 years of age and non-diabetic were interviewed using pre-designed, pre-tested questionnaire for their socio-demographic profile and lifestyle. Fasting Blood glucose of all study subjects were done to screen undiagnosed diabetics. Statistical analysis used: Centers for Disease Control (CDC), Epi Info TM 7.2.3.1 was used. Pearson’s Chi Square were applied. Results: 33.4% were found to have high diabetes risk. Risk of diabetes increases with age. 7.6% of the study subjects were found to be diabetic and were unaware of their diabetic status. Physical inactivity and increasing waist circumference were found to be significantly associated with risk of diabetes. Diabetes risk was also significantly associated with positive family history. Conclusions: Screening and early identification of high risk individuals would help in early diagnosis and treatment to prevent or to delay the onset of diabetes mellitus and its complications

Optimizing Agricultural Supply Chains with Machine Learning Algorithms

Agricultural supply chains serve as the vital link between producers and consumers, ensuring the efficient flow of agricultural products. Their optimization is essential to address challenges like seasonal variations, transportation complexities, and quality control. Machine learning, with its predictive modeling, demand forecasting, route optimization, inventory management, quality control, and risk management capabilities, offers a promising solution to revolutionize the agricultural industry. These supply chains consist of various components, including producers, distributors, retailers, and consumers, each contributing to the network that delivers agricultural products. To enhance efficiency and product quality, innovative solutions are required to overcome challenges such as seasonal fluctuations and quality concerns. Machine learning empowers supply chain stakeholders to make data-driven decisions, automate processes, and optimize various aspects of the supply chain. This technology enhances the resilience and efficiency of agricultural supply chains, ensuring the delivery of fresh and safe products to consumers. Effective data collection and preprocessing are essential for leveraging machine learning's potential. Through sourcing, cleaning, and structuring data from diverse sources, stakeholders enable machine learning algorithms to make informed recommendations and predictions. Machine learning's application in agricultural supply chains, exemplified by predictive modeling for crop yield through weather data analysis and disease detection, illustrates the power of data-driven technologies in enhancing crop production, reducing losses, and ensuring a secure global food supply

Antibodies to Enteroviruses in Cerebrospinal Fluid of Patients with Acute Flaccid Myelitis.

Acute flaccid myelitis (AFM) has caused motor paralysis in >560 children in the United States since 2014. The temporal association of enterovirus (EV) outbreaks with increases in AFM cases and reports of fever, respiratory, or gastrointestinal illness prior to AFM in >90% of cases suggest a role for infectious agents. Cerebrospinal fluid (CSF) from 14 AFM and 5 non-AFM patients with central nervous system (CNS) diseases in 2018 were investigated by viral-capture high-throughput sequencing (VirCapSeq-VERT system). These CSF and serum samples, as well as multiple controls, were tested for antibodies to human EVs using peptide microarrays. EV RNA was confirmed in CSF from only 1 adult AFM case and 1 non-AFM case. In contrast, antibodies to EV peptides were present in CSF of 11 of 14 AFM patients (79%), significantly higher than controls, including non-AFM patients (1/5 [20%]), children with Kawasaki disease (0/10), and adults with non-AFM CNS diseases (2/11 [18%]) (P = 0.023, 0.0001, and 0.0028, respectively). Six of 14 CSF samples (43%) and 8 of 11 sera (73%) from AFM patients were immunoreactive to an EV-D68-specific peptide, whereas the three control groups were not immunoreactive in either CSF (0/5, 0/10, and 0/11; P = 0.008, 0.0003, and 0.035, respectively) or sera (0/2, 0/8, and 0/5; P = 0.139, 0.002, and 0.009, respectively).IMPORTANCE The presence in cerebrospinal fluid of antibodies to EV peptides at higher levels than non-AFM controls supports the plausibility of a link between EV infection and AFM that warrants further investigation and has the potential to lead to strategies for diagnosis and prevention of disease

Identification of a Novel Cetacean Polyomavirus from a Common Dolphin (Delphinus delphis) with Tracheobronchitis

A female short-beaked common dolphin calf was found stranded in San Diego, California in October 2010, presenting with multifocal ulcerative lesions in the trachea and bronchi. Viral particles suggestive of polyomavirus were detected by EM, and subsequently confirmed by PCR and sequencing. Full genome sequencing (Ion Torrent) revealed a circular dsDNA genome of 5,159 bp that was shown to form a distinct lineage within the genus Polyomavirus based on phylogenetic analysis of the early and late transcriptomes. Viral infection and distribution in laryngeal mucosa was characterised using in-situ hybridisation, and apoptosis observed in the virus-infected region. These results demonstrate that polyomaviruses can be associated with respiratory disease in cetaceans, and expand our knowledge of their diversity and clinical significance in marine mammals

Longitudinal molecular microbial analysis of influenza-like illness in New York City, may 2009 through may 2010

<p>Abstract</p> <p>Background</p> <p>We performed a longitudinal study of viral etiology in samples collected in New York City during May 2009 to May 2010 from outpatients with fever or respiratory disease symptoms in the context of a pilot respiratory virus surveillance system.</p> <p>Methods</p> <p>Samples were assessed for the presence of 13 viruses, including influenza A virus, by MassTag PCR.</p> <p>Results</p> <p>At least one virus was detected in 52% of 940 samples analyzed, with 3% showing co-infections. The most frequently detected agents were rhinoviruses and influenza A, all representing the 2009 pandemic H1N1 strain. The incidence of influenza H1N1-positive samples was highest in late spring 2009, followed by a decline in summer and early fall, when rhinovirus infections became predominant before H1N1 reemerged in winter. Our study also identified a focal outbreak of enterovirus 68 in the early fall of 2009.</p> <p>Conclusion</p> <p>MassTag multiplex PCR affords opportunities to track the epidemiology of infectious diseases and may guide clinicians and public health practitioners in influenza-like illness and outbreak management. Nonetheless, a substantial proportion of influenza-like illness remains unexplained underscoring the need for additional platforms.</p

Identification of a novel nidovirus in an outbreak of fatal respiratory disease in ball pythons (Python regius)

Background: Respiratory infections are important causes of morbidity and mortality in reptiles; however, the causative agents are only infrequently identified. Findings: Pneumonia, tracheitis and esophagitis were reported in a collection of ball pythons (Python regius). Eight of 12 snakes had evidence of bacterial pneumonia. High-throughput sequencing of total extracted nucleic acids from lung, esophagus and spleen revealed a novel nidovirus. PCR indicated the presence of viral RNA in lung, trachea, esophagus, liver, and spleen. In situ hybridization confirmed the presence of intracellular, intracytoplasmic viral nucleic acids in the lungs of infected snakes. Phylogenetic analysis based on a 1,136 amino acid segment of the polyprotein suggests that this virus may represent a new species in the subfamily Torovirinae. Conclusions: This report of a novel nidovirus in ball pythons may provide insight into the pathogenesis of respiratory disease in this species and enhances our knowledge of the diversity of nidoviruses