Clinical and Radiological Outcomes of Patients With Anterior Acetabulum Fractures Treated by the Modified Stoppa Approach.

Introduction Acetabular fractures are intra-articular fractures involving the lower extremity's weight-bearing dome. These fractures require an anatomical reduction of the fracture fragments. This aim can be accomplished by the selection of an appropriate surgical approach. This study aimed to analyze the clinical and radiological outcomes of patients with fractures in the anterior part of the acetabulum who were treated by the modified Stoppa approach. Methods This prospective observational study was conducted from April 2022 to September 2023. The inclusion criteria were: (i) age between 18 and 70 years, (ii) displaced acetabular fracture (displacement > 3 mm), (iii) within three weeks of trauma (iv) acetabular fractures with involvement of anterior column. Exclusion criteria included: (i) patients with visceral injuries requiring colostomy, (ii) pathological fracture, (iii) open fractures of the acetabulum, and (iv) neglected fracture (more than three weeks). Intraoperative data regarding surgical time, amount of blood loss, and incidence of intraoperative complications were recorded. In the postoperative period, anteroposterior X-ray and Judet views of the pelvis X-ray were obtained. Matta criteria were used to judge the quality of Fracture reduction and fixation. All the patients to be included in this study had undergone a minimum follow-up duration of six months. At the last follow-up, an assessment of the functional outcome of the affected hip by Merle d'Aubigné Hip Score and Harris Hip Score was done. Results Twenty-four patients were included in the study. The mean patient age was 36.08±11.65 years. Eighteen patients were male (75%) and six patients were female in this study. All acetabular fractures were due to high-energy trauma: road traffic accidents in 22 cases (91%) and fall from height in two cases (9%). According to Judet & Letournel's classification, there were 13 T-type fractures, five transverse fractures, and six associated both column fractures. The mean duration of surgery was 152.08 ±29.19 minutes, and the mean intraoperative blood loss was 277.08±85.95 ml. Intraoperatively one unit of blood transfusion was done in most cases. There were intraoperative complications of rent in the external iliac vein in two patients. Postoperative X-rays showed anatomical reduction in 17 cases, imperfect reduction in five cases, and poor reduction in two cases. Functional outcome of the hip by Merle d'Aubigné Hip Score was very good in 15, good in four, fair in three, and poor in two patients. Similar functional outcomes were obtained with the Harris Hip Score. Conclusion The results of the current study demonstrated that the modified Stoppa approach allows good visualization of the pelvic brim, quadrilateral surface, and posterior column. Lesser experienced orthopedic surgeons should utilize this approach to get good radiological and functional outcomes.

Vaccine Hesitancy in India: Facilitators and Inhibitors.

COVID-19 is yet not completely over; however, many people are hesitant to take COVID-19 vaccines despite their availability. Vaccine hesitancy is a major roadblock to attaining normalcy and controlling the spread of the COVID-19 virus. The present research used a multitheoretical framework (Health Belief Model, 3Cs framework, fatalism, and religious fatalism) to comprehend the complexity of vaccine hesitancy. Thus, the present study aimed at exploring vaccine hesitancy in India by using key components of the Health Belief Model, 3Cs framework, fatalism, religious fatalism, and some demographics as predictors. Data were collected electronically with the help of Google Forms from 639 Indian adults following snowballing and convenience sampling techniques with standardized measures (albeit some modifications to suit the context of the study). Descriptive analysis and hierarchical regression analysis were run in SPSS (V-22) to analyze the data. Results revealed that participants of the present study scored relatively high on vaccine hesitancy. Muslims as compared with Hindus and vaccination status emerged as significant predictors of vaccine hesitancy out of the demographic factors. Fear of COVID-19, vaccine convenience, and religious fatalism also significantly predicted vaccine hesitancy. Thus, a comprehensive approach is needed to strategically use these predictors to control vaccine hesitancy.

Dynamics of soil bio-physicochemical properties under different disturbance regimes in sal forests in western Himalaya, India.

Disturbance is a key factor in controlling vegetation diversity, nutrient influx rate, and biochemical cycling in terrestrial forest ecosystems. Limited studies are available on changes in tree diversity, soil nutrients and enzyme activities in response to different intensities of land disturbances in the Himalayan forests. Present study investigated the impact of varying intensities of disturbances on tree diversity and their relationship with soil physical and bio-chemical properties in sal forests, Western Himalayas. Sites were categorized into four different classes of disturbances, namely, No disturbance (ND), Low disturbance (LD), Moderate disturbance (MD), and High disturbance (HD). Composite samples were collected at two depths (0-15 and 15-30 cm) in each plot to investigate soil physical and biochemical properties. Multivariate analyses were conducted to find relationship between tree vegetation and soil physical and biochemical properties. Soil organic carbon (Corg), total nitrogen (Nttl), available phosphorous (Pavl), microbial biomass carbon (Cmic), nitrogen (Nmic), phosphorous (Pmic), and enzymes (dehydrogenase (DHA), Urease, acid and alkaline phosphatase) followed the order: MD > ND > LD > HD. Across disturbances, soil physical and biochemical characteristics significantly (p < 0.05) decreased with increasing soil depths. Across the sites, positive correlation was observed among soil microbial biomass, enzymes, Corg, clay, and moisture. Redundancy analysis (RDA) results revealed that species distribution is essential regulator in the variation of prominent soil variables, viz., nutrients (Nttl and Pavl), Cmic, and DHA across disturbance categories and soil depths. Moreover, variance partitioning analysis (VPA) showed that changes in vegetation composition across disturbance levels explain 13.12 % of the variation in soil biochemical subset higher than soil physicochemical subset. The result illustrated that moderate disturbance increases species composition, soil nutrient properties and microbial activity. These findings would help understand microbial activity and its relationship with disturbances, suggesting site-specific measurements for soil nutrient availability and above-below ground interactions.

Analysis of Cataract Surgery Instrument Identification Performance of Convolutional and Recurrent Neural Network Ensembles Leveraging BigCat.

Purpose: To develop a method for accurate automated real-time identification of instruments in cataract surgery videos. Methods: Cataract surgery videos were collected at University of Michigan's Kellogg Eye Center between 2020 and 2021. Videos were annotated for the presence of instruments to aid in the development, validation, and testing of machine learning (ML) models for multiclass, multilabel instrument identification. Results: A new cataract surgery database, BigCat, was assembled, containing 190 videos with over 3.9 million annotated frames, the largest reported cataract surgery annotation database to date. Using a dense convolutional neural network (CNN) and a recursive averaging method, we were able to achieve a test F1 score of 0.9528 and test area under the receiver operator characteristic curve of 0.9985 for surgical instrument identification. These prove to be state-of-the-art results compared to previous works, while also only using a fraction of the model parameters of the previous architectures. Conclusions: Accurate automated surgical instrument identification is possible with lightweight CNNs and large datasets. Increasingly complex model architecture is not necessary to retain a well-performing model. Recurrent neural network architectures add additional complexity to a model and are unnecessary to attain state-of-the-art performance. Translational Relevance: Instrument identification in the operative field can be used for further applications such as evaluating surgical trainee skill level and developing early warning detection systems for use during surgery.

Central nervous system immune interactome is a function of cancer lineage, tumor microenvironment, and STAT3 expression.

BACKGROUNDImmune cell profiling of primary and metastatic CNS tumors has been focused on the tumor, not the tumor microenvironment (TME), or has been analyzed via biopsies.METHODSEn bloc resections of gliomas (n = 10) and lung metastases (n = 10) were analyzed via tissue segmentation and high-dimension Opal 7-color multiplex imaging. Single-cell RNA analyses were used to infer immune cell functionality.RESULTSWithin gliomas, T cells were localized in the infiltrating edge and perivascular space of tumors, while residing mostly in the stroma of metastatic tumors. CD163+ macrophages were evident throughout the TME of metastatic tumors, whereas in gliomas, CD68+, CD11c+CD68+, and CD11c+CD68+CD163+ cell subtypes were commonly observed. In lung metastases, T cells interacted with CD163+ macrophages as dyads and clusters at the brain-tumor interface and within the tumor itself and as clusters within the necrotic core. In contrast, gliomas typically lacked dyad and cluster interactions, except for T cell CD68+ cell dyads within the tumor. Analysis of transcriptomic data in glioblastomas revealed that innate immune cells expressed both proinflammatory and immunosuppressive gene signatures.CONCLUSIONOur results show that immunosuppressive macrophages are abundant within the TME and that the immune cell interactome between cancer lineages is distinct. Further, these data provide information for evaluating the role of different immune cell populations in brain tumor growth and therapeutic responses.FUNDINGThis study was supported by the NIH (NS120547), a Developmental research project award (P50CA221747), ReMission Alliance, institutional funding from Northwestern University and the Lurie Comprehensive Cancer Center, and gifts from the Mosky family and Perry McKay. Performed in the Flow Cytometry & Cellular Imaging Core Facility at MD Anderson Cancer Center, this study received support in part from the NIH (CA016672) and the National Cancer Institute (NCI) Research Specialist award 1 (R50 CA243707). Additional support was provided by CCSG Bioinformatics Shared Resource 5 (P30 CA046592), a gift from Agilent Technologies, a Research Scholar Grant from the American Cancer Society (RSG-16-005-01), a Precision Health Investigator Award from University of Michigan (U-M) Precision Health, the NCI (R37-CA214955), startup institutional research funds from U-M, and a Biomedical Informatics & Data Science Training Grant (T32GM141746).

Seizure Types Classification by Generating Input Images With in-Depth Features From Decomposed EEG Signals for Deep Learning Pipeline.

Electroencephalogram (EEG) based seizure types classification has not been addressed well, compared to seizure detection, which is very important for the diagnosis and prognosis of epileptic patients. The minuscule changes reflected in EEG signals among different seizure types make such tasks more challenging. Therefore, in this work, underlying features in EEG have been explored by decomposing signals into multiple subcomponents which have been further used to generate 2D input images for deep learning (DL) pipeline. The Hilbert vibration decomposition (HVD) has been employed for decomposing the EEG signals by preserving phase information. Next, 2D images have been generated considering the first three subcomponents having high energy by involving continuous wavelet transform and converting them into 2D images for DL inputs. For classification, a hybrid DL pipeline has been constructed by combining the convolution neural network (CNN) followed by long short-term memory (LSTM) for efficient extraction of spatial and time sequence information. Experimental validation has been conducted by classifying five types of seizures and seizure-free, collected from the Temple University EEG dataset (TUH v1.5.2). The proposed method has achieved the highest classification accuracy up to 99% along with an F1-score of 99%. Further analysis shows that the HVD-based decomposition and hybrid DL model can efficiently extract in-depth features while classifying different types of seizures. In a comparative study, the proposed idea demonstrates its superiority by displaying the uppermost performance.

Classification of Epileptic Seizure From EEG Signal Based on Hilbert Vibration Decomposition and Deep Learning.

A convolution neural network (CNN) architecture has been designed to classify epileptic seizures based on two-dimensional (2D) images constructed from decomposed mono-components of electroencephalogram (EEG) signals. For the decomposition of EEG, Hilbert vibration decomposition (HVD) has been employed. In this work, four brain rhythms - delta, theta, alpha, and beta have been utilized to obtain the mono-components. Certainly, the data-driven CNN model is most efficient for 2D image processing and recognition. Therefore, 2D images have been generated from one-dimensional (1D) decomposed mono-components by employing continuous wavelet transform (CWT). Next, simultaneous multiple input images in parallel have been directly fed into the CNN pipeline for feature extraction and classification. For evaluation, the EEG dataset provided by the Bonn University has been taken into consideration. Further, a 5-fold cross-validation technique has been applied to obtain generalized and robust classification performance. The average classification accuracy, sensitivity, and specificity reached up to 98.6%, 97.2%, and 100% respectively. The results show that the proposed idea is very much efficient in seizure classification. The proposed idea resourcefully combines the advantages of HVD and CNN to classify epileptic seizures from EEG signal.

Seizure Type Classification Using EEG Based on Gramian Angular Field Transformation and Deep Learning.

classification of seizure types plays a crucial role in diagnosis and prognosis of epileptic patients which has not been addressed properly, while most of the works are surrounded by seizure detection only. However, in recent times, few works have been attempted on the classification of seizure types using deep learning (DL). In this work, a novel approach based on DL has been proposed to classify four types of seizures - complex partial seizure, generalized non-specific seizure, simple partial seizure, tonic-clonic seizure, and seizure-free. Certainly, one of the most efficient classes of DL, convolution neural network (CNN) has achieved exemplary success in the field of image recognition. Therefore, CNN has been employed to perform both automatic feature extraction and classification tasks after generating 2D images from 1D electroencephalogram (EEG) signal by employing an efficient technique, called gramian angular summation field. Next, these images fed into CNN to perform binary and multiclass classification tasks. For experimental evaluation, the Temple University Hospital (TUH, v1.5.2) EEG dataset has been taken into consideration. The proposed method has achieved classification accuracy for binary and multiclass - 3, 4, and 5 up to 96.01%, 89.91%, 84.19%, and 84.20% respectively. The results display the potentiality of the proposed method in seizure type classification.Clinical relevance-gramian angular summation field, seizure types, convolution neural network.

Epigenetically defined therapeutic targeting in H3.3G34R/V high-grade gliomas.

High-grade gliomas with arginine or valine substitutions of the histone H3.3 glycine-34 residue (H3.3G34R/V) carry a dismal prognosis, and current treatments, including radiotherapy and chemotherapy, are not curative. Because H3.3G34R/V mutations reprogram epigenetic modifications, we undertook a comprehensive epigenetic approach using ChIP sequencing and ChromHMM computational analysis to define therapeutic dependencies in H3.3G34R/V gliomas. Our analyses revealed a convergence of epigenetic alterations, including (i) activating epigenetic modifications on histone H3 lysine (K) residues such as H3K36 trimethylation (H3K36me3), H3K27 acetylation (H3K27ac), and H3K4 trimethylation (H3K4me3); (ii) DNA promoter hypomethylation; and (iii) redistribution of repressive histone H3K27 trimethylation (H3K27me3) to intergenic regions at the leukemia inhibitory factor (LIF) locus to drive increased LIF abundance and secretion by H3.3G34R/V cells. LIF activated signal transducer and activator of transcription 3 (STAT3) signaling in an autocrine/paracrine manner to promote survival of H3.3G34R/V glioma cells. Moreover, immunohistochemistry and single-cell RNA sequencing from H3.3G34R/V patient tumors revealed high STAT3 protein and RNA expression, respectively, in tumor cells with both inter- and intratumor heterogeneity. We targeted STAT3 using a blood-brain barrier­penetrable small-molecule inhibitor, WP1066, currently in clinical trials for adult gliomas. WP1066 treatment resulted in H3.3G34R/V tumor cell toxicity in vitro and tumor suppression in preclinical mouse models established with KNS42 cells, SJ-HGGx42-c cells, or in utero electroporation techniques. Our studies identify the LIF/STAT3 pathway as a key epigenetically driven and druggable vulnerability in H3.3G34R/V gliomas. This finding could inform development of targeted, combination therapies for these lethal brain tumors.

Examining the role of emotional intelligence as a moderator for virtual communication and decision making effectiveness during the COVID-19 crisis: revisiting task technology fit theory.

The COVID 19 has brought unprecedented changes in the way we communicate. There is a greater accent on Virtual communication. This paper aims to establish a relationship between Emotional intelligence and the effectiveness of Virtual communication on Decision making. This empirical study is based on a sample drawn from 296 working professionals at five different levels of organizational hierarchy. A standardized questionnaire (ɑ = 0.824) was used to collect the responses of Emotional intelligence, Virtual communication, and Decision-making effectiveness. Hierarchical regression using PROCESS Macro model 1 was used to identify the moderating effect of Emotional intelligence on Virtual communication and Decision making effectiveness. Since the p-value (p ≤ .007) is found significant, Emotional intelligence acts as a moderator that affects the strength of the relationship between Virtual communication effectiveness and Decision making. Validation of Task Technology fit theory is the theoretical implication of the study. Manipulation of individual dimensions in the model can reduce the dependence on technology for task completion with enhanced performance effectiveness. The findings are relevant to educators, consultants, and any professional who need to adapt Virtual communication platforms on an ongoing basis. Since work-life balance is projected as a constraint in this study, policymakers can consider policy amendments to reduce the stress caused due to Virtual communication which intrudes into their personal space. This empirical study is the first of its kind to benchmark the organizational practice of Emotional intelligence training to enhance Virtual communication and Decision making effectiveness during unprecedented times of pandemic.

An Efficacy and Safety Study of Remogliflozin in Obese Indian Type 2 Diabetes Mellitus Patients Who Were Inadequately Controlled on Insulin Glargine Plus other Oral Hypoglycemic Agents.

AIMS & OBJECTIVES: The objective of this retrospective study was to investigate the efficacy of adding remogliflozin to current insulin glargine plus two oral drug i.e. metformin and teneligliptin therapy in poorly controlled Indian type 2 diabetes. MATERIALS AND METHODS: 173 study participants were initially selected from patient database who continued on their insulin glargine or received an increased dose of insulin glargine along with other OHA based therapy (Group A) and 187 were selected who had received remogliflozin (100 mg BD) (Group B) in addition to insulin glargine along with other OHA based therapy. Glycated haemoglobin (HbA1c), total daily insulin dose, body weight, and the number of hypoglycemic events were recorded at weeks 0, 12 and 24. RESULTS: During the study, mean values of HbA1c, FBG and P2BG were significantly reduced in both groups. Insulin requirements decreased from 45.8 ± 16.7 IU/day to 38.5 ± 13.5 IU/day at week 12 (P < 0.001) and at week 24 even further decreased to 29.5 ± 14.5 IU/Day. Twenty three patients in group B were able to cease insulin treatment altogether after 24 week treatment. It has been observed that to attain tight blood glucose control, we need to increase insulin dose in group A from 45.5 ± 16.5 IU/Day to 51.5 ± 14.5 at week 12 (P<0.01), which further increased to 53.8 ± 12.8 IU/Day at week 24 (P<0.01). Adding remogliflozin showed significant effect on blood pressure (P < 0.001) and weight reduction (P < 0.001). It has been observed that 38% patients achieved targeted HbA1c (≤7%) in group B where it was 22% in group A. CONCLUSION: Results demonstrate that in uncontrolled T2DM patients, remogliflozin 100 mg BD can successfully lay a foundation for prolonged good glycemic control. Early addition of remogliflozin with insulin glargine plus OHAs may be an alternative compared to intensive up titration of insulin daily dose in people with uncontrolled T2DM.

Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer.

The role of tumor infiltrating lymphocytes (TILs) as a biomarker to predict disease progression and clinical outcomes has generated tremendous interest in translational cancer research. We present an updated and enhanced deep learning workflow to classify 50x50 um tiled image patches (100x100 pixels at 20x magnification) as TIL positive or negative based on the presence of 2 or more TILs in gigapixel whole slide images (WSIs) from the Cancer Genome Atlas (TCGA). This workflow generates TIL maps to study the abundance and spatial distribution of TILs in 23 different types of cancer. We trained three state-of-the-art, popular convolutional neural network (CNN) architectures (namely VGG16, Inception-V4, and ResNet-34) with a large volume of training data, which combined manual annotations from pathologists (strong annotations) and computer-generated labels from our previously reported first-generation TIL model for 13 cancer types (model-generated annotations). Specifically, this training dataset contains TIL positive and negative patches from cancers in additional organ sites and curated data to help improve algorithmic performance by decreasing known false positives and false negatives. Our new TIL workflow also incorporates automated thresholding to convert model predictions into binary classifications to generate TIL maps. The new TIL models all achieve better performance with improvements of up to 13% in accuracy and 15% in F-score. We report these new TIL models and a curated dataset of TIL maps, referred to as TIL-Maps-23, for 7983 WSIs spanning 23 types of cancer with complex and diverse visual appearances, which will be publicly available along with the code to evaluate performance. Code Available at: https://github.com/ShahiraAbousamra/til_classification.

Density-based classification in diabetic retinopathy through thickness of retinal layers from optical coherence tomography.

Diabetic retinopathy (DR) is a severe retinal disorder that can lead to vision loss, however, its underlying mechanism has not been fully understood. Previous studies have taken advantage of Optical Coherence Tomography (OCT) and shown that the thickness of individual retinal layers are affected in patients with DR. However, most studies analyzed the thickness by calculating summary statistics from retinal thickness maps of the macula region. This study aims to apply a density function-based statistical framework to the thickness data obtained through OCT, and to compare the predictive power of various retinal layers to assess the severity of DR. We used a prototype data set of 107 subjects which are comprised of 38 non-proliferative DR (NPDR), 28 without DR (NoDR), and 41 controls. Based on the thickness profiles, we constructed novel features which capture the variation in the distribution of the pixel-wise retinal layer thicknesses from OCT. We quantified the predictive power of each of the retinal layers to distinguish between all three pairwise comparisons of the severity in DR (NoDR vs NPDR, controls vs NPDR, and controls vs NoDR). When applied to this preliminary DR data set, our density-based method demonstrated better predictive results compared with simple summary statistics. Furthermore, our results indicate considerable differences in retinal layer structuring based on the severity of DR. We found that: (a) the outer plexiform layer is the most discriminative layer for classifying NoDR vs NPDR; (b) the outer plexiform, inner nuclear and ganglion cell layers are the strongest biomarkers for discriminating controls from NPDR; and (c) the inner nuclear layer distinguishes best between controls and NoDR.

Diabetes and COVID-19: A Review.

Coronavirus Disease 2019 (COVID-19) is an emerging disease and since its first identification in Wuhan, China, in December 2019, there has been a rapid increase in cases and deaths across the world. COVID-19 has been shown to have an immense impact in infected persons with diabetes, worsening their outcome, especially in elderly, smokers, obese, those having CVD, CKD, poor glycemic control and long duration of diabetes. In this review we summarize the current understanding of `the impact of COVID-19 on diabetes and discusses the pathophysiological mechanisms and management of diabetes and its complication in this scenario.

Magnetic Resonance Elastography of the Liver in Children and Adolescents: Assessment of Regional Variations in Stiffness.

RATIONALE AND OBJECTIVES: We describe our experience in measuring parenchyma stiffness across the liver Couinaud segments in lieu of the conventional practice of using a single slice-wise "global" region-of-interest. We hypothesize that the heterogeneous nature of fibrosis can lead to regional stiffness within the organ, and that it can be reflected by Couinaud segment-based magnetic resonance elastography measurements. MATERIALS AND METHODS: This retrospective study involved from 173 patients (116 males, 57 females, 1.0-22.5 years, 14.7 ± 3.5 years) who underwent exams between June 2017 and September 2018. Liver stiffness across the eight Couinaud segments was measured in addition to a single-slice global measurement by two analysts. Inter- and intrarater analysis was performed in a subset of 20 cases. Individual segment stiffness values, the average across the segments, and the coefficients of variation (CoV) were compared to global single-slice-derived values using linear and Lin's concordance correlation coefficients. Linear correlations between stiffness values versus age, gender, and body-mass-index (BMI) were also evaluated. RESULTS: We observed CoVs ranging from 3.1%-79.2%, 17.2 ± 7.2%. The CoV was not correlated with age or BMI (r2 < 0.01, p = 0.99 for both). The CoV did not differ between males (17.1 ± 5.6%) and females (17.3 ± 9.8%) (p = 0.88). There were no correlations between global stiffness versus age (r2 = 0.02, p = 0.84) or BMI (r2 = 0.03, p = 0.68). A range of 0.58-0.86 was observed for Lin's concordance correlation coefficient between segmental stiffness, the average stiffness across segments, and global stiffness. Segments II and VII had the highest frequency of being the stiffest Couinaud segment. The average stiffness across the segments correlated strongly with the single-slice global measurement (r2 = 0.88, p< 0.01). CONCLUSION: There exists potential variations in parenchyma stiffness across the liver Couinaud segments, which may reflect the heterogeneous nature of fibrosis. This variation can potentially provide additional diagnostic and clinical information.

Clinical experience with insulin detemir, biphasic insulin aspart and insulin aspart in people with type 2 diabetes: Results from the East India cohort of the A1chieve study.

BACKGROUND: The A1chieve, a multicentric (28 countries), 24-week, non-interventional study evaluated the safety and effectiveness of insulin detemir, biphasic insulin aspart and insulin aspart in people with T2DM (n = 66,726) in routine clinical care across four continents. MATERIALS AND METHODS: Data was collected at baseline, at 12 weeks and at 24 weeks. This short communication presents the results for patients enrolled from East India. RESULTS: A total of 2177 patients were enrolled in the study. Four different insulin analogue regimens were used in the study. Patients had started on or were switched to biphasic insulin aspart (n=1605), insulin detemir (n=230), insulin aspart (n=233), basal insulin plus insulin aspart (n=49) and other insulin combinations (n=54). At baseline glycaemic control was poor for both insulin naïve (mean HbA1c: 8.9%) and insulin user (mean HbA1c: 9.1%) groups. After 24 weeks of treatment, both the groups showed improvement in HbA1c (insulin naïve: -1.6%, insulin users: -1.6%). SADRs including major hypoglycaemic events or episodes did not occur in any of the study patients. CONCLUSION: Starting or switching to insulin analogues was associated with improvement in glycaemic control with a low rate of hypoglycaemia.