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Objective: Diabetes mellitus has been recognized as one of the comorbidities that predict the severity of illness in patients infected with COVID-19. The characteristics of patients presenting with diabetic ketoacidosis (DKA) and COVID-19 infection have not been described. Methods: We describe 5 patients with DKA and concomitant COVID-19 admitted to the intensive care unit of an academic medical center. Three patients had type 1 diabetes mellitus, and 2 patients had type 2 diabetes mellitus. Results: While DKA with an infectious etiology is a common presentation, we observed that the patients with DKA precipitated by COVID-19 presented with atypical symptoms. COVID-19 infection was revealed during search for an etiology of DKA. Conclusion: It is prudent to have a low threshold to screen for COVID-19 infection in patients with DKA.

Etched fiber Bragg grating (eFBG) sensors have shown to be highly sensitive with the capability of giving accurate real‐time response to a variety of measurands such as pressure, gas, biomolecules, to name a few. These sensors have not shown their mettle as competitive products mainly due to nonreproducibility in results and inefficiency in upscaling for large‐scale production; the main reason being nonuniform and complicated coating procedures. Herein, the enhancement in refractive index (RI) sensitivity (≈4 times) obtained with electrospinning of polyvinyl alcohol–reduced graphene oxide (PVA–rGO) nanofibers onto eFBG sensor using a customized target and a unique sandwich arrangement is demonstrated. The enhancement in RI sensitivity has led to a lower detection limit and increased sensitivity and linear range for a case study using myoglobin (Mb), an early‐stage cardiac biomarker with high reproducible results (standard error ≤±2.3%). rGO embedded PVA nanofiber electrospun onto an eFBG sensor (PVA–rGO sensor) is the first of its kind and has significant importance in developing cost‐effective, label‐free, multianalyte, portable, real‐time, point‐of‐care (POC) kits at ambient conditions.

Background: Early-onset dementia (EOD) defined as dementia with clinical onset before the age of 65 years, has estimated proportion ranging up to 45.3%. Although EOD leads to severe psychosocial consequences that affect people in their latter part of working age, the literature from India is limited. Objective: The aim of this study is to investigate the profile of patients with EOD attending Geriatric Clinic and Services, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India. Materials and Methodology: All records of patients attending the Geriatric Clinic and Services, diagnosed with EOD between January 2017 and June 2018 with their details pertaining to sociodemographic, clinical, risk factors, and behavioral problems were examined. Results: Of the 320 patients with cognitive complaints seen during the period of 18 months, 108 (33.75%) patients had a diagnosis of EOD. The mean age at onset of illness was 55.38 (Standard deviation - 6.53) years (range - 34–65 years). Of these 58 (53.6%) patients found to have Alzheimer's dementia (AD), 31 (28.7%) have fronto-temporal dementia (FTD), 6 (5.5%) have vascular dementia (VaD), 3 (2.7%) patients have Parkinson's disease-related dementia, and 6 (5.5%) have unspecified dementia. Discussion: During the 18 months, the EOD patients constituted one-third of all dementia patients visiting Geriatric Clinic. Degenerative etiology was the main diagnostic cluster. The most common type was AD, similar to senile type of dementia, was followed by FTD and VaD. The study showed a delay of 3.18 years in seeking consultation. Conclusion: EODs seems to have higher degenerative etiology and with higher associated behavioral and psychological symptoms. There is a need for setting up specialized memory clinics.

Purpose: To propose an automated approach for detecting and classifying Intracranial Hemorrhages (ICH) directly from sinograms using a deep learning framework. This method is proposed to overcome the limitations of the conventional diagnosis by eliminating the time-consuming reconstruction step and minimizing the potential noise and artifacts that can occur during the Computed Tomography (CT) reconstruction process., Methods: This study proposes a two-stage automated approach for detecting and classifying ICH from sinograms using a deep learning framework. The first stage of the framework is Intensity Transformed Sinogram Sythesizer, which synthesizes sinograms that are equivalent to the intensity-transformed CT images. The second stage comprises of a cascaded Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) model that detects and classifies hemorrhages from the synthesized sinograms. The CNN module extracts high-level features from each input sinogram, while the RNN module provides spatial correlation of the neighborhood regions in the sinograms. The proposed method was evaluated on a publicly available RSNA dataset consisting of a large sample size of 8652 patients., Results: The results showed that the proposed method had a notable improvement as high as 27% in patient-wise accuracies when compared to state-of-the-art methods like ResNext-101, Inception-v3 and Vision Transformer. Furthermore, the sinogram-based approach was found to be more robust to noise and offset errors in comparison to CT image-based approaches. The proposed model was also subjected to a multi-label classification analysis to determine the hemorrhage type from a given sinogram. The learning patterns of the proposed model were also examined for explainability using the activation maps., Conclusion: The proposed sinogram-based approach can provide an accurate and efficient diagnosis of ICH without the need for the time-consuming reconstruction step and can potentially overcome the limitations of CT image-based approaches. The results show promising outcomes for the use of sinogram-based approaches in detecting hemorrhages, and further research can explore the potential of this approach in clinical settings., (© 2023 American Association of Physicists in Medicine.)

Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects. These implants are usually generated offline and may require days to weeks to be available. An automated implant design process combined with onsite manufacturing facilities can guarantee immediate implant availability and avoid secondary intervention. To address this need, the AutoImplant II challenge was organized in conjunction with MICCAI 2021, catering for the unmet clinical and computational requirements of automatic cranial implant design. The first edition of AutoImplant (AutoImplant I, 2020) demonstrated the general capabilities and effectiveness of data-driven approaches, including deep learning, for a skull shape completion task on synthetic defects. The second AutoImplant challenge (i.e., AutoImplant II, 2021) built upon the first by adding real clinical craniectomy cases as well as additional synthetic imaging data. The AutoImplant II challenge consisted of three tracks. Tracks 1 and 3 used skull images with synthetic defects to evaluate the ability of submitted approaches to generate implants that recreate the original skull shape. Track 3 consisted of the data from the first challenge (i.e., 100 cases for training, and 110 for evaluation), and Track 1 provided 570 training and 100 validation cases aimed at evaluating skull shape completion algorithms at diverse defect patterns. Track 2 also made progress over the first challenge by providing 11 clinically defective skulls and evaluating the submitted implant designs on these clinical cases. The submitted designs were evaluated quantitatively against imaging data from post-craniectomy as well as by an experienced neurosurgeon. Submissions to these challenge tasks made substantial progress in addressing issues such as generalizability, computational efficiency, data augmentation, and implant refinement. This paper serves as a comprehensive summary and comparison of the submissions to the AutoImplant II challenge. Codes and models are available at https://github.com/Jianningli/Autoimplant_II., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Michal Španěl is affiliated with TESCAN 3DIM that sponsored the challenge., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Diet-induced models of chronic kidney disease (CKD) offer several advantages, including clinical relevance and animal welfare, compared with surgical models. Oxalate is a plant-based, terminal toxic metabolite that is eliminated by the kidneys through glomerular filtration and tubular secretion. An increased load of dietary oxalate leads to supersaturation, calcium oxalate crystal formation, renal tubular obstruction, and eventually CKD. Dahl-Salt-Sensitive (SS) rats are a common strain used to study hypertensive renal disease; however, the characterization of other diet-induced models on this background would allow for comparative studies of CKD within the same strain. In the present study, we hypothesized that SS rats on a low-salt, oxalate rich diet would have increased renal injury and serve as novel, clinically relevant and reproducible CKD rat models. Ten-week-old male SS rats were fed either 0.2% salt normal chow (SS-NC) or a 0.2% salt diet containing 0.67% sodium oxalate (SS-OX) for five weeks.Real-time PCR demonstrated an increased expression of inflammatory marker interleukin-6 (IL-6) ( p < 0.0001) and fibrotic marker Timp-1 metalloproteinase ( p < 0.0001) in the renal cortex of SS-OX rat kidneys compared with SS-NC. The immunohistochemistry of kidney tissue demonstrated an increase in CD-68 levels, a marker of macrophage infiltration in SS-OX rats ( p < 0.001). In addition, SS-OX rats displayed increased 24 h urinary protein excretion (UPE) ( p < 0.01) as well as significant elevations in plasma Cystatin C ( p < 0.01). Furthermore, the oxalate diet induced hypertension ( p < 0.05). A renin-angiotensin-aldosterone system (RAAS) profiling (via liquid chromatography-mass spectrometry; LC-MS) in the SS-OX plasma showed significant ( p < 0.05) increases in multiple RAAS metabolites including angiotensin (1-5), angiotensin (1-7), and aldosterone. The oxalate diet induces significant renal inflammation, fibrosis, and renal dysfunction as well as RAAS activation and hypertension in SS rats compared with a normal chow diet. This study introduces a novel diet-induced model to study hypertension and CKD that is more clinically translatable and reproducible than the currently available models.

Male infertility is solely responsible for 20-30% of infertility cases. Oxidative damage of sperm DNA is positively linked with oligoasthenoteratozoospermia (OAT), and male infertility. The antioxidants are being explored worldwide to combat OAT, sperm DNA fragmentation and reactive oxygen species. The objective of the study was to assess the effectiveness of an antioxidant blend in improving sperm count, semen parameters and reducing DNA fragmentation index (DFI) in sub-fertile males. A prospective, double-blind, randomized, placebo-controlled trial was conducted in 300 sub-fertile males (25-45 years) from ten study sites in India. Subjects were randomized in either the antioxidant blend treatment group or placebo group. We assessed changes in sperm count, motility, normal morphology, semen volume, and percent DFI before and after treatment (90 days). To further stratify data on different criteria post hoc analysis was performed. Statistical analysis was performed using SPSS 10.0 software. There were improvements in sperm count, semen volume, sperm motility, and sperm normal morphology in the treatment group. There was improvement in sperm count in severe oligospermia subjects (sperm count < 5 million/mL, 5-10 million/mL, 10.1-15 million/mL), and high-extremely higher baseline DFI (20-30%, 31-40% and above 40%), as per post hoc analysis. There was no premature discontinuation and adverse events were reported during the study, indicating safety and well-tolerability of treatment. Study results confirmed the well-researched fact of antioxidants being effective to reduce oxidative stress and thus improve sperm DNA integrity and also improved semen parameters in males aged 40 and above., Trial Registration: Clinical Trials Registry-India Identifier: CTRI/2020/12/029590., Competing Interests: Conflict of Interest: - Authors: Rohit Shelatkar is a part of Vitabiotics UK. Meyer Organics Pvt. Ltd. is a group company of Vitabiotics. Other authors declare no conflict of interest - Reviewers: Nothing to declare - Editors: Nothing to declare, (Copyright © 2023 Translational and Clinical Pharmacology.)

Background and Purpose: Very few large scale multicentric stroke clinical trials have been done in India. The Indian Council of Medical Research funded INSTRuCT (Indian Stroke Clinical Trial Network) as a task force project with the objectives to establish a state-of-the-art stroke clinical trial network and to conduct pharmacological and nonpharmacological stroke clinical trials relevant to the nation and globally. The purpose of the article is to enumerate the structure of multicentric stroke network, with emphasis on its scope, challenges and expectations in India., Methods: Multiple expert group meetings were conducted by Indian Council of Medical Research to understand the scope of network to perform stroke clinical trials in the country. Established stroke centers with annual volume of 200 patients with stroke with prior experience of conducting clinical trials were included. Central coordinating center, standard operating procedures, data and safety monitoring board were formed., Discussion: In first phase, 2 trials were initiated namely, SPRINT (Secondary Prevention by Structured Semi-Interactive Stroke Prevention Package in India) and Ayurveda treatment in the rehabilitation of patients with ischemic stroke in India (RESTORE [Rehabilitation of Ischemic stroke Patients in India: A Randomized controlled trial]). In second phase, 4 trials have been approved. SPRINT trial was the first to be initiated. SPRINT trial randomized first patient on April 28, 2018; recruited 3048 patients with an average of 128.5 per month so far. The first follow-up was completed on May 27, 2019. RESTORE trial randomized first patient on May 22, 2019; recruited 49 patients with an average of 3.7 per month so far. The first follow-up was completed on August 30, 2019., Conclusions: In next 5 years, INSTRuCT will be able to complete high-quality large scale stroke trials which are relevant globally., Registration: URL: http://www.ctri.nic.in/; Unique Identifier: CTRI/2017/05/008507.

Fringe projection profilometry (FPP) is a widely used non-contact optical method for 3D profiling of objects. The commonly used linear fringe pattern in FPP has periodic intensity variations along the lateral direction. As a result, the linear fringe pattern used in FPP cannot uniquely represent the lateral shift induced by the objects having surface discontinuities. Thus, unambiguous surface profiling of objects, especially with surface discontinuities, using a single linear fringe image having a single fringe frequency, is unfeasible. This paper proposes using a radially symmetric circular fringe pattern as the structured light pattern for accurate unambiguous surface profiling of sudden height-discontinuous objects. To the best of our knowledge, this is the only method that can reconstruct discontinuous height profiles with the help of a single fringe image having a single frequency. The performance of the proposed algorithm is evaluated on several synthetic and real objects having smooth variations and discontinuities. Compared to the well-known fringe projection methods, the results depict that for a tolerable range of error, the proposed method can be applied for the reconstruction of objects with 4 times higher dynamic range and even at much lower fringe frequencies.

Disease in agricultural field is a big problem that causes a massive loss in production. In this present investigation, we have reported a soil-borne bacterium Bacillus cereus IB311 which is antagonistic to plant pathogens (Pseudomonas syringae and Agrobacterium tumefaciens), and could make a substantial contribution to the prevention of plant diseases. To prove the practical application, the strain was directly applied in agricultural field. The results demonstrated that B. cereus IB311 has increased the production (20% and 26% in term of average pod number per plant, average seed number per pod, and seed yield per experimental plot) in ground nut (Arachis hypogaea var. Koushal, G201) and sesame (Sesamum indicum var. Kanak), respectively. To reduce the production cost, the biomass production was optimized through response surface methodology (RSM). Interactions of three variables (glucose, beef extract and inoculum) were studied using Central Composite Design. According to our analysis, optimum production of Bacillus cereus IB311 (5.383 µg/ mL) may be obtained at glucose 1.985%, beef extract 1.615% and inoculums size 0.757%. Therefore, we strongly believe that the application of this strain in agricultural field as bio-controlling agent will definitely enhance the production yield and will reduce the disease risk.

Normal human brain exhibits approximately bi-fold symmetry with respect to its midsagittal plane (MSP). The objective of this work is to investigate the effect of doubling atlases (i.e., reference images) used in multi-atlas fusion methods by exploiting the inherent bilateral symmetry of human brain. To this end, we perform automated segmentation of 15 subcortical structures using Local Weighted Voting (LWV) fusion method with varying number of atlases. We consider three specific scenarios for atlases while performing fusion: (i) fusion with original OASIS atlases, (ii) with atlases obtained by flipping the original atlases based on their MSP, and (iii) with both original and flipped atlases. Evaluations are performed on the publicly available OASIS dataset of 20 normal human brain MR images. One of the key findings of this study is that when the number of atlases available for fusion is less than 10, fusion by combining both the original and flipped atlases provided more accurate segmentations than using only the original atlases, or only the flipped atlases.

In recent years, fusing segmentation results obtained based on multiple template images has become a standard practice in many medical imaging applications. Such multiple-templates-based methods are found to provide more reliable and accurate segmentations than the single-template-based methods. In this paper, we present a new approach for learning prior knowledge about the performance parameters of template images using the local intensity similarity information; we also propose a methodology to incorporate that prior knowledge through the estimation of the optimal MAP parameters. The proposed method is evaluated in the context of segmentation of structures in the brain magnetic resonance images by comparing our results with some of the state-of-the-art segmentation methods. These experiments have clearly demonstrated the advantages of learning and incorporating prior knowledge about the performance parameters using the proposed method.

We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multichannel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

This paper presents a new and original variational framework for atlas-based segmentation. The proposed framework integrates both the active contour framework, and the dense deformation fields of optical flow framework. This framework is quite general and encompasses many of the state-of-the-art atlas-based segmentation methods. It also allows to perform the registration of atlas and target images based on only selected structures of interest. The versatility and potentiality of the proposed framework are demonstrated by presenting three diverse applications: In the first application, we show how the proposed framework can be used to simulate the growth of inconsistent structures like a tumor in an atlas. In the second application, we estimate the position of nonvisible brain structures based on the surrounding structures and validate the results by comparing with other methods. In the final application, we present the segmentation of lymph nodes in the Head and Neck CT images, and demonstrate how multiple registration forces can be used in this framework in an hierarchical manner., (Copyright © 2011 Elsevier B.V. All rights reserved.)